Skip to main content
NCBI home page
Ecology and Evolution logoLink to Ecology and Evolution
. 2017 Sep 7;7(20):8316–8328. doi: 10.1002/ece3.3386

Population structure and genetic diversity of Bromus tectorum within the small grain production region of the Pacific Northwest

Nevin C Lawrence 1,2,✉, Amber L Hauvermale 1, Amit Dhingra 3, Ian C Burke 1
PMCID: PMC5648657  PMID: 29075451

Abstract

Bromus tectorum L. is an invasive winter annual grass naturalized across the United States. Numerous studies have investigated B. tectorum population structure and genetics in the context of B. tectorum as an ecological invader of natural areas and rangeland. Despite the wealth of information regarding B. tectorum, previous studies have not focused on, or made comparisons to, B. tectorum as it persists in individual agroecosystems. The objectives of this study were to assess the genetic diversity and structure, the occurrence of generalist and specialist genotypes, and the influence of climate on distribution of B. tectorum sourced exclusively from within small grain production regions of the Pacific Northwest. Genetic diversity of B. tectorum sourced from agronomic fields was found to be similar to what has been observed from other land use histories. Six distinct genetic clusters of B. tectorum were identified, with no evidence to indicate that any of the genetic clusters were better adapted to a particular geographical area or climate within the region. Given the apparent random spatial distribution of B. tectorum genetic clusters at the spatial scale of this analysis, unique genotypes may be well mixed within region, similar to what was reported for other inbreeding weedy grass species.

Keywords: agroecosystem, genotype‐by‐sequencing, postinvasion biology, single nucleotide polymorphism

1. INTRODUCTION

Agricultural weeds represent the ecological and evolutionary response of human crop cultivation to native and introduced flora (Neve, Vila‐Aoib, & Roux, 2009). Anthropogenic impacts associated with agriculture lead to fragmentation and simplification of natural ecosystems at multiple scales. The yearly disturbance of tillage, planting, and herbicide applications may impact how evolutionary forces such as genetic drift, selection, and breeding systems act against weed species in a different way than previously observed in rangeland or natural areas (Thrall et al., 2011). Neve et al. (2009) argues modern weed management requires an approach based in evolutionary biology, of which the first step is understanding “the extent, structure, and significance of genetic variation.”

Downy brome (Bromus tectorum) is a widely distributed weed across North America, and the population genetics of the species has been well characterized. Despite the wealth of information regarding the genetic diversity of downy brome, previous studies have not focused on, or made comparisons of downy brome genetic structure in agronomic fields. The lack of downy brome genetic studies within agroecosystems is significant given that downy brome is a widely distributed and a serious pest in small grains and other crops across North America.

Previous studies have solely investigated downy brome population genetics in the context of downy brome as an ecological invader. Consequently, previous research has focused on the prevalence of common versus rare genotypes across the landscape, genetic differences between native populations of Eurasia and invasive populations of North America, and evidence of local adaptation to distinct ecosystems (Leger et al., 2009; Merrill, Meyer, & Coleman, 2012; Novak & Mack, 1993, 2016; Novak, Mack, & Soltis, 1991, 1993; Scott et al., 2010). Novak et al. (1991) reported that a limited number of genotypes were found distributed widely across North America. In comparing native and introduced populations, total genetic diversity across the entire native range was higher than the introduced range. However, within a population, genetic diversity was greater in the introduced range (Novak & Mack, 1993, 2016). Genetic differences between native and introduced ranges can be explained by the founder effect reducing genetic diversity in the introduced range coupled with mixed populations of selfing individuals from diverse origins (Novak & Mack, 1993, 2016).

While widely spread genotypes across the introduced range can be attributed to generalists, evidence for local adaptation to an environment by specialist genotypes has been reported for downy brome. When local adaption was observed, variation in phenological traits including flowering time, vernalization requirements, and timing of mature seed set was identified as driving adaptation (Ball, Frost, & Gitelman, 2004; Meyer, Nelson, & Carlson, 2004; Rice & Mack, 1991a,b). Ramakrishnan et al. (2006) found that ecological distance better predicted genetic distance of populations than physical distance, indicating that similar habitats select for similar genotypes from widely dispersed genotypes. Downy brome has been observed invading new habitats as both broadly adapted generalist genotypes and pre‐adapted specialist genotypes (Scott et al., 2010). When characterizing genotypes in the Intermountain West, historically invaded land has been largely occupied by generalist genotypes, while recently invaded land was dominated by distinct specialist genotypes (Merrill et al., 2012; Scott et al., 2010).

Previous studies have identified low genetic diversity within the species downy brome, with mean expected heterozygosity ranging from 0.002 to 0.336 within populations (Bartlett, Novak, & Mack, 2002; Meyer et al., 2013). While heterozygous individuals have been reported in the literature, outcrossing is exceedingly rare (Leger et al., 2009; Meyer et al., 2013; Novak & Mack, 1993; Valliant, Mack, & Novak, 2007). A common garden experiment was designed to encourage and quantify outcrossing at greater frequencies than would be expected in nature, and outcrossing was observed at 0.75% (Meyer et al., 2013).

The PNW small grain production can be divided into four unique agroecological classes: annual crop, annual crop‐fallow transition, crop‐fallow, and irrigated crop. Annual crop, annual crop‐fallow transition, and crop‐fallow classes are all dryland cropping systems with winter wheat as the principle crop, bringing the most economic value, within the rotation. The division of dryland agroecological classes is predominantly driven by total annual rainfall. Annual crop land can support a crop in each year of the rotation, annual crop‐fallow transition land can support a crop in 2 out of 3 years of a rotation, and crop‐fallow land can only support a crop in 1 of 2 years. When irrigation is available winter wheat is still grown, but rotational crops become more diverse and winter wheat is not a principle rotational crop (Huggins et al., 2012). As the amount of moisture, both through precipitation and irrigation, increases so does the intensity of the cropping systems. Within the PNW small grain production region downy brome generalist genotypes would be expected throughout the entirety of the region, while specialists would be expected at greater abundance and frequency between different agroecological zones.

A genotype‐by‐sequencing approach was used in this study to estimate population structure and determine whether the genetic state of downy brome in PNW agroecosystems is similar to previous studies where individuals were sourced from nonagronomic locations. Study objectives were as follows: (1) to assess the genetic variability of downy brome sourced exclusively from within small grain production fields, (2) determine the frequency and occurrence of generalists versus specialist genotypes, and (3) determine the influence of climate on the distribution of genotypes.

2. METHODS

2.1. Sampling of plant materials

Downy brome is distributed ubiquitously within agronomic fields of the PNW. No large landscape features, such as mountain ranges, are present that could block gene flow. Climate of the region exists across a longitudinal gradient with annual precipitation in the region ranging from <300 mm to >600 mm, with precipitation increasing from the west to east. Mean annual temperature also varies on an west to east gradient, with the western portion at 11°C and decreasing to 5°C to the east based on a 30‐year average (1971; 2000) (Huggins et al., 2012). To accommodate for studies of both population genetics and structure, and for future field studies investigating climate and phenology, a systematic random sampling design was used to efficiently maximize the geographical area represented (Strofer et al., 2007).

A 10‐km grid was laid over the small grain production region, and a point was randomly assigned for sampling within each grid. One hundred and ninety total sampling points were generated. If the sampling point was not located in a small grain field, the sampling point was moved to the nearest small grain field. If there was no field within 3 km of the original sampling point, the location was discarded. Following re‐assignment of the original sampling locations, 130 sampling locations were retained (Figure 1).

Figure 1.

Figure 1

Open in a new tab

The small grain production region of the inland Pacific Northwest

Due to limited resources, the number of sampling locations was emphasized at the expense of collecting fewer individuals at each location (Ward & Jasieniuk, 2009). Collecting a single individual from approximately evenly spaced locations is an appropriate sampling method under the following condition: (1) the species is evenly distributed across the entire study area, (2) there are no known barriers to gene flow, (3) multilocus genetic data are used, and (4) newer Bayesian genetic clustering techniques are employed to determine genetic structure (Guillot et al., 2005, 2009; Manel et al., 2007; Strofer et al., 2007). The aforementioned criteria were satisfied given the biology of downy brome, the study location, and the methodology employed.

In June of 2010 and 2011, a single downy brome plant was collected as either mature panicles or a live plant from each of 130 re‐assigned sampling locations. Each plant was collected at least 10 m from the field border. Live plants were transplanted into a greenhouse and allowed to grow until mature panicles could be collected. Caryopses from collected panicles were later germinated to provide tissue for DNA extraction. On 21 March 2014, as plants were at the two‐ to three‐leaf stage, a single ~4‐cm leaf was collected from 95 (Table 1) of the 130 emerged downy brome collections for DNA extraction. A related species to downy brome, Bromus diandrus Roth (ripgut brome), was included as a control to determine whether population structure analysis could detect the related species as an outlier. DNA was extracted using a BioSprint 96 Plant Kit and BioSprint 96 workstation (Qiagen, Valencia, CA). DNA was quantified with the PicoGreen® assay (Invitrogen”, Carlsbad, CA) using a Synergy” HT (BioTek®, Winooski, VT) microplate reader.

Table 1.

Accession ID number, GPS coordinates of collection locations, year of collection, and cluster membership as determined by DAPC of each accession

Accession Longitude Latitude Year Agroecosystem Accession Longitude Latitude Year Agroecosystem
1 −119.215 46.387 2010 Fallow 49 −118.37 46.677 2011 Fallow
2 −118.989 46.825 2011 Fallow 50 −119.853 46.671 2010 Fallow
3 −116.466 46.252 2010 Irrigation 51 −118.31 47.456 2011 Fallow
4 −118.916 47.785 2011 Fallow 52 −119.605 46.182 2010 Fallow
5 −120.938 45.673 2010 Fallow 53 −117.949 46.429 2010 Irrigation
6 −118.6 45.995 2011 Fallow 54 −117.748 47.903 2011 Fallow
7 −120.646 46.4 2010 Intermediate 55 −117.872 47.214 2010 Fallow
8 −118.794 46.744 2011 Annual 56 −119.935 45.388 2010 Annual
9 −118.098 46.37 2010 Fallow 57 −117.674 47.124 2010 Annual
10 −118.785 47.475 2011 Irrigation 58 −119.175 47.42 2011 Fallow
11 −120.489 45.482 2010 Irrigation 59 −117.477 46.953 2010 Fallow
12 −118.403 45.76 2010 Irrigation 60 −119.218 46.561 2010 Irrigation
13 −120.184 45.62 2010 Fallow 61a −117.162 46.375 2011 Irrigation
14 −118.358 46.335 2011 Fallow 62 −119.15 47.179 2011 Fallow
15 −120.336 46.886 2011 Intermediate 63 −117.251 47.39 2010 Annual
16 −117.883 47.515 2010 Annual 64 −118.992 45.765 2011 Annual
17 −116.87 46.396 2011 Irrigation 65 −116.71 46.917 2010 Fallow
18 −118.127 46.656 2011 Irrigation 66 −119.049 46.999 2011 Irrigation
19 −119.872 47.102 2011 Irrigation 67 −115.963 46.1 2010 Fallow
20 −118.135 47.686 2011 Irrigation 68 −119.164 47.99 2011 Irrigation
21 −119.851 46.737 2011 Irrigation 69 −120.616 45.469 2010 Irrigation
22 −117.804 46.629 2010 Fallow 70 −118.61 46.082 2011 Fallow
23 −119.441 45.638 2010 Annual 71 −120.699 46.676 2010 Fallow
24 −118.464 47.49 2011 Annual 72 −118.651 46.769 2011 Annual
25 −119.864 46.263 2010 Fallow 73 −120.655 46.559 2010 Fallow
26 −117.632 47.715 2011 Irrigation 74 −118.851 47.523 2011 Irrigation
27 −119.281 45.761 2010 Irrigation 75 −120.561 46.462 2010 Fallow
28a −117.378 47.263 2010 Fallow 76 −118.436 46.006 2011 Irrigation
29 −119.279 46.708 2011 Fallow 77 −120.164 46.261 2010 Fallow
30 −117.165 47.102 2010 Annual 78 −118.363 47.235 2011 Fallow
31 −119.32 47.468 2011 Annual 79 −120.412 46.996 2011 Intermediate
32 −117.092 47.484 2010 Fallow 80 −117.661 46.898 2010 Annual
33 −119.691 46.742 2010 Irrigation 81 −120.13 46.375 2010 Irrigation
34 −116.836 46.924 2010 Irrigation 82 −118.168 46.803 2011 Irrigation
35 −119.078 47.203 2011 Irrigation 83 −119.799 45.353 2010 Fallow
36 −120.965 45.483 2010 Fallow 84 −117.906 46.394 2010 Fallow
37 −118.859 46.478 2010 Intermediate 85a −119.711 47.337 2011 Irrigation
38 −120.746 45.635 2010 Annual 86 −118.18 46.913 2011 Intermediate
39 −118.742 46.343 2011 Annual 87 −119.373 46.107 2010 Annual
40b −120.184 46.041 2010 Fallow 88 −120.241 46.003 2010 Irrigation
41 −118.642 47.261 2011 Fallow 89 −119.411 46.849 2011 Irrigation
42 −120.358 45.419 2010 Irrigation 90 −117.518 46.492 2010 Fallow
43 −118.679 47.796 2011 Fallow 91 −119.241 46.029 2010 Fallow
44 −120.162 45.396 2010 Fallow 92 −117.551 47.524 2011 Fallow
45 −118.491 46.138 2011 Intermediate 93 −119.199 47.014 2011 Fallow
46 −119.908 46.702 2010 Annual 94 −117.245 47.307 2010 Intermediate
47 −118.465 47.49 2011 Fallow 95 −119.37 47.888 2011 Annual
48 −120.346 45.895 2010 Fallow 96 −118.895 46.669 2011 Fallow
Open in a new tab
a

Accession were removed from further analysis following GBS.

b

Accession is Bromus diandrus Roth.

2.2. Genotype‐by‐sequencing

A reduced representation genotype‐by‐sequencing (GBS) approach was employed to identify SNP molecular markers (Elshire et al., 2011). A modified GBS protocol developed by Mascher et al. (2013) for use with semiconductor sequencing platform was followed. Amplicons were sequenced on an Ion Proton” sequencer using an Ion P1” Chip (Life Technologies, Carlsbad, CA). Sequencing data were obtained in FASTQ file format, and the file size ranged from 5 to 112 MBs with an average size of 45.6 MBs. Average sequence length was 100 bp, and all sequences were trimmed to 100 bp using FASTX to provide a uniform sequence length for SNP calling.

2.3. SNP calling

SNP calling was conducted using Stacks (1.22, Cresko Laboratory, Eugene, OR) (Catchen et al., 2013). The Stacks program aligns identical or nearly identical sequence reads into “Stacks” across individuals, and a catalog file is written. Each locus from each individual is matched against the catalog to determine the allelic state at each locus in each individual, while filtering and discarding poor‐quality reads. As there is no reference genome available for Bromus tectorum, the Perl script denovo_map.pl was used to call SNPs using default settings (Catchen et al., 2013). Called SNPs were filtered using the populations command in Stacks. Default parameters were used, with the exception of requiring a minimum stack depth of 5, and all loci to be found in 75% of individuals to ensure the validity of obtained markers.

2.4. Analysis of population structure and genetic diversity

The output from stacks was analyzed in R (R Development Core Team2014) using the package adegenet (Jombart, 2008; Jombart & Ahmed, 2011). Using the adegenet package, discriminant analysis of principal components (DAPC) (Jombart, Devillard, & Balloux, 2010) was used to describe population structure of collected downy brome accessions. DAPC consists of two general steps. Principal component analysis (PCA) is first used to find the optimal number of clusters (k), based on genetic similarity and upon Bayesian information criterion (BIC), and to initially assign individuals to each cluster. In the second step, synthetic variables called linear discriminants, consisting of linear combinations of alleles, are used to discriminate the cluster membership of each individual. SNPs which are retained by the DAPC, due to their value in discriminating cluster membership of individual accessions, can be considered “more informative SNPs” and will be referred to as such throughout the manuscript.

To complement cluster assignments based upon DAPC, the fixation index (F ST) between each genetic cluster was calculated (Nei, 1973) along with genetic distance using Nei's standard (Nei, 1972, 1978) using the R package “adegenet.” A dendrogram was then constructed from the resulting genetic distance matrix using the R package “ape” (Paradis, Claude, & Strimmer, 2004; Popescu, Huber, & Paradis, 2012). These further analyses were conducted as they retain the full complement of filtered SNPs, in contrast to the DAPC analysis which only retains a subset of the available genetic markers.

2.5. Population genetic metrics

In order to make comparisons with previous studies of Bromus tectorum genetics, and the genetics of the weed grass species, Bromus sterilis L and Setaria sp., observed and expected heterozygosity, genetic diversity, and genetic partitioning (G ST and G’ST) were calculated across clusters using the R Package mmod (Bartlett et al., 2002; Godt & Hamrick, 1998; Green et al., 2001; Novak & Mack, 1993; Novak et al., 1991; Valliant et al., 2007; Wang, Wendell, & Dekker, 1995a,b; Winter, 2012).

Population genetic metrics were also calculated for downy brome accessions based upon the agroecological class that the samples were taken from. Given the geographical separation between sampling locations, it is unlikely that recent gene flow occurred between any of the accession. As such, any accessions grouped together for the purpose of calculating population genetic metrics cannot be considered true populations. However, calculating heterozygosity, genetic diversity, and genetic partitioning based upon cluster assignment, and the land use of the sampling locations, may aid in the detection of specialist or generalist genotypes across the landscape and compliment the DAPC analysis.

3. RESULTS

3.1. Reduced representation sequencing

Raw reads per accession ranged from 51,740 to 1,030,188 (Table 2). After trimming and filtering retained reads ranged from 741 to 13,985, per accession, from which 16,382 SNPs were initially called. SNPs that were then found in at least 75% of individuals were retained for further analysis, resulting in 384 SNPs being selected. The number of retained reads and SNPs was not uniformly retained among accessions (Table 2). The DAPC approaches employed only a subset of genetic markers which were retained for further analysis. The retained SNPs for the DAPC analysis were well distributed across all accessions. Calculated population genetic metrics utilized all SNPs including those which were not well represented across all accessions. If poor SNP representation across all accessions may bias the analyses, then it would likely be detected by disagreements between the multivariate and other employed analyses.

Table 2.

Accession ID number, raw reads before filtering, retained reads, total SNPs called from retained reads, SNPs remaining after filtering for polymorphisms present in at least 75% of individuals, and cluster assignment

Acc Raw reads Retained reads Total SNPs Filtered SNPs Cluster Acc Raw reads Retained reads Total SNPs Filtered SNPs Cluster
1 311,630 8,074 659 306 3 49 235,449 5,908 471 266 4
2 491,261 12,203 1,256 316 4 50 87,350 1,404 128 109 7
3 366,270 11,716 1,040 272 3 51 306,382 10,284 757 326 1
4 254,196 7,184 621 312 7 52 201,723 4,999 400 328 4
5 265,962 6,913 647 318 5 53 335,053 10,012 889 342 1
6 202,765 6,559 501 326 3 54 77,955 1,013 117 108 4
7 383,398 12,755 1,275 240 3 55 267,913 7,325 562 250 4
8 515,094 13,985 1,257 196 4 56 323,872 2,976 751 94 4
9 383,953 10,947 920 256 4 57 120,012 2,637 197 123 4
10 213,236 5,319 431 300 1 58 235,521 5,459 534 286 4
11 231,857 6,938 576 302 3 59 222,875 6,358 467 286 4
12 178,755 5,214 364 310 4 60 231,163 7,663 652 290 4
13 239,374 6,621 479 332 2 61a 46,569 ¯ ¯ ¯ ¯
14 157,885 4,320 376 260 4 62 330,976 9,553 514 230 7
15 437,108 13,123 1,344 336 7 63 245,397 7,543 514 230 1
16 274,703 7,643 533 328 3 64 144,011 2,306 215 118 4
17 201,318 5,472 477 334 7 65 89,231 1,499 121 93 4
18 249,234 7,282 557 342 1 66 111,615 2,168 184 126 2
19 255,478 7,388 551 342 4 67 159,917 3,596 341 274 1
20 224,248 6,494 491 320 4 68 271,135 8,287 666 326 1
21 285,603 8,568 710 326 2 69 180,719 3,762 319 161 5
22 161,500 3,501 282 278 4 70 320,809 8,639 841 288 4
23 398,914 3,849 254 244 4 71 79,085 1,076 93 69 4
24 177,992 4,873 386 282 4 72 145,681 2,695 255 138 5
25 105,832 1,925 164 114 5 73 184,404 5,349 406 290 7
26 329,257 10,028 881 282 3 74 167,620 4,181 317 274 2
27 246,356 6,123 526 310 7 75 137,565 2,845 248 246 4
28a 25,870 – – – – 76 94,942 1,278 131 118 4
29 325,619 10,460 941 310 4 77 104,684 1,839 144 128 7
30 216,171 5,684 468 320 4 78 89,507 1,107 116 112 2
31 98,193 1,025 114 77 4 79 232,352 6,132 569 254 4
32 160,074 4,240 271 168 4 80 132,611 2,493 253 142 1
33 225,757 5,875 395 286 4 81 227,168 5,839 501 304 5
34 211,269 6,194 489 302 3 82 204,385 6,089 452 284 5
35 86,627 941 107 105 4 83 128,435 2,760 267 157 2
36 145,952 3,051 286 144 3 84 73,498 749 118 105 2
37 401,706 11,465 1,034 322 7 85a 31,665 – – – –
38 174,952 3,774 334 302 7 86 193,443 4,682 391 260 1
39 117,666 1,855 176 113 4 87 147,353 3,353 346 280 4
40b 216,529 5,557 415 214 6 88 226,010 5,679 594 318 7
41 301,680 8,453 702 354 7 89 139,471 2,862 215 212 4
42 260,351 6,941 590 344 1 90 129,576 2,367 297 296 4
43 446,979 11,679 1,219 256 4 91 85,637 1,229 207 186 1
44 90,759 1,952 138 101 4 92 146,609 2,525 271 268 7
45 91,137 1,424 114 110 1 93 155,940 3,154 306 298 4
46 138,555 2,556 246 238 7 94 363,292 11,190 955 308 2
47 146,041 2,753 212 111 4 95 113,052 2,659 250 238 4
48 323,731 8,858 780 308 4 96 177,273 4,368 398 304 4
Open in a new tab

Acc, Accession ID.

a

Accession were removed from further analysis following GBS due to poor quality.

b

Accession is Bromus diandrus Roth.

3.2. Discriminant analysis of principal components

Thirty‐five principal components were retained, corresponding to roughly 85% of cumulative variance, and used to identify seven clusters as optimal based upon BIC value. Following determination of the optimal number of clusters, multiple DAPC simulations identified six principal components as ideal in assigning group membership without overfitting the model. Three linear discriminants were retained to calculate the probability of group membership, and individuals were assigned accordingly. All clusters with the exception of cluster six contained multiple individuals. Cluster 6 contains the ripgut brome individuals (Table 1), as would be expected for the outlier individual.

The distribution of individuals and clusters across the first and second discriminant function (Figure 2a) indicate separation of clusters 3, 6, and 7. Cluster 2 overlapped considerably with cluster 4, as did cluster 1 with cluster 5. When individuals and clusters were distributed on the first and third discriminant function (Figure 2b) clusters 2, 5, 6 and 7 were separated, and cluster 1 overlapped with cluster 4. The distribution of clusters and individuals across the second and third discriminant function (Figure 2c) indicate overlap of the cluster 3 and 4 while clusters 1, 2, 5, 6 and 7 are distributed and distinct. Regardless of which discriminant functions were used to describe distribution, cluster 6 is the most distinct cluster. Cluster 4, however, overlaps with cluster 1, 2, and 3 depending on the linear discriminants used to describe the distribution of individuals.

Figure 2.

Figure 2

Open in a new tab

Distribution of individuals and clusters across the first, second, and third linear discriminates; PCA eigenvalues is the cumulative variance explained by the six retained principal components; DA eigenvalues represents which linear discriminants are being compared in each scatter plot, with the height of each bar representing the relative contribution in explaining total variance; scatter plot a represents linear discriminant 1, x‐axis, and linear discriminate 2, y‐axis; scatter plot b represents linear discriminant 1, x‐axis, and linear discriminate 3, y‐axis; scatter plot c represents linear discriminant 2, x‐axis, and linear discriminate 3, y‐axis; each point on each scatter plot represents an individual; each color is used to distinguish a separate cluster, which is identified by number; the ellipses around each number represent were 67% of the variance of each cluster assuming a bivariate distribution

F ST values (Table 3) between each cluster reflect the differentiation between clusters described by DAPC in Figure 2. In other words, as the F ST approaches zero there is a greater likelihood that clusters exhibit low levels of genetic differentiation and should not be considered separate from one and another. Small F ST values were returned for cluster 4 in relation to all other clusters, 0.003–0.057, excluding cluster 6. While the sequences containing the most informative SNPs were found across all downy brome clusters, cluster 4 did not contain any of the polymorphisms of the sequences retained by DAPC. The lack of identifying SNPs for cluster 4 explains the limited dispersion of cluster 4 and low pairwise F ST values. Cluster 6, which contained the single ripgut brome accession, was more dispersed across the linear discriminants relative to other clusters, and the dispersion indicated by DAPC was also represented by F ST values (Table 3).

Table 3.

Pairwise F ST values of the 7 described genetic clusters

Fixation index (F ST)
1 2 3 4 5 6
2 0.226 – – – – –
3 0.148 0.187 – – – –
4 0.006 0.003 0.006 – – –
5 0.146 0.364 0.238 0.057 – –
6 0.751 0.713 0.805 0.134 0.680 –
7 0.151 0.162 0.121 0.011 0.258 0.705
Open in a new tab

Pairwise genetic distance values among accessions, when viewed as a dendrogram (Figure 3), resulted in a similar grouping of accessions as the DAPC analysis. Little differentiation, based upon genetic distance, is observed between clusters 1, 2, 4, 5, and 7. Cluster 6, as reflected by F ST values, is an outlier; however, cluster 3 also appears distinct from all other clusters. The genetic distance between cluster 3 and all other clusters (Figure 3) is also illustrated in Figure 2a,b, but not Figure 2c. The spatial distribution of all individuals, color coded by assigned cluster, indicated no easy‐to‐interpret patterns of spatial distribution (Figure 4).

Figure 3.

Figure 3

Open in a new tab

Dendrogram, calculated using Nei's genetic distance, illustrating the relatedness of the seven described genetic clusters. Each branch, running vertical, represents an accession, while horizontal bars and numbers designate cluster membership

Figure 4.

Figure 4

Open in a new tab

Spatial distribution of individuals and cluster membership as determined by discriminate analysis of principal component

3.3. Heterozygosity, genetic diversity, and genetic partitioning

Heterozygosity was calculated for each individual loci and averaged across all accession, and across each cluster to calculate within‐cluster genetic diversity (H S), total diversity (H T), and the ratio of genetic diversity partitioned among clusters (G ST and G’ST) using the R Package “mmod” (Hedrick, 2005; Nei, 1973; Winter, 2012). Observed heterozygosity (H O) at individual loci averaged across all accessions ranged from 0.0 to 0.65 with a mean value of 0.006. Across all accessions, expected heterozygosity at each loci (H E) ranged from 0.021 to 0.667 with a mean value of 0.2. Within‐cluster expected heterozygosity ranged from 0.069 to 0.144 with an average of 0.122 (H E) (Table 4). Within‐cluster genetic diversity was 0.085, and total genetic diversity across clusters was 0.267. Genetic partitioning was analyzed between clusters with cluster 6, the outlier cluster, removed. Partitioning of genetic diversity within and among clusters was calculated as 0.680 and 0.785, using G ST and G’ST, respectively, indicating that a majority of genetic diversity is partitioned among genetic clusters.

Table 4.

Genetic diversity of Bromus tectorum collected from the small grain production region of the PNW

Genetic diversity by cluster
Cluster H O H E
1 0.028 0.076
2 0.058 0.110
3 0.029 0.144
4 0.076 0.069
5 0.056 0.138
6a 0.287 0.144
7 0.025 0.101
H S H T G ST G’ST
0.085 0.267 0.680 0.785
Genetic diversity by Agroecosystem
Class H O H E
Annual 0.011 0.161
Transition 0.007 0.244
Fallow 0.004 0.129
Irrigated 0.005 0.249
H S H T G ST G’ST
0.203 0.215 0.057 0.094
Open in a new tab

H O, observed heterozygosity; H E, expected heterozygosity; H S, within‐cluster genetic diversity; H T, total diversity; G ST and G’ST, ratio of genetic diversity partitioned among population calculated using different mathematical formulas.

a

Cluster six includes the single individual of the species Bromus diandrus, which was excluded in calculating H S, H T, G ST, G’ST and from calculation of genetic diversity by Agroecosystem Class.

When heterozygosity was calculated for accessions grouped by the agroecological class, there were no substantial differences compared to when accessions were grouped by cluster. However, there was a large difference in genetic partitioning (Table 4). The majority of genetic diversity was partitioned within agroecologic classes, as indicated by G ST and G’ST values of 0.057 and 0.094, respectively. Comparing the results of genetic partitioning between accessions grouped by cluster and accessions grouped by agroecological classes, accessions within clusters are genetically similar, while agroecological class from where an accession was sourced has limited influence on genetic properties across the PNW.

4. DISCUSSION

Consistent with other studies, downy brome collected from small grain production fields in the PNW does not appear to have greater genetic diversity than populations in nonagronomic settings (Ashley & Longland, 2009; Bartlett et al., 2002; Meyer et al., 2013; Novak & Mack, 1993; Novak et al., 1991; Ramakrishnan et al., 2002; Valliant et al., 2007). Previous studies utilizing 25 allozyme markers reported observed heterozygosity ranging from 0.000 to 0.002 and expected heterozygosity ranging from 0.0 to 0.032 (Bartlett et al., 2002; Novak & Mack, 1993; Novak et al., 1991; Valliant et al., 2007). Later studies utilizing seven simple sequence repeat (SSR) markers reported greater genetic diversity compared to previous work with allozymes with observed heterozygosity ranging from 0.000 to 0.011 and expected heterozygosity ranging from 0.018 to 0.547 (Ashley & Longland, 2009; Kao, Brown, & Hufbauer, 2008; Ramakrishnan et al., 2002). Compared to the allozyme and microsatellite data, Meyer et al. (2013) reported greater observed heterozygosity, 0.001–0.009, and expected heterozygosity, 0.149–0.336, using 93 SNP markers. Within the PNW small grain production region, the average observed heterozygosity, 0.05, was greater than previous research using allozymes and SSR makers but similar to Meyer et al. (2013). Average expected heterozygosity within the PNW, 0.085, was between what was reported from allozyme and SSR marker data sets (Table 3). Higher observed heterozygosity would be expected from Meyer et al. (2013) as the sampled populations had been chosen because high rates of outcrossing were expected, based upon previous sampling which indicated relatively high heterozygosity and genetic diversity within the populations.

G ST values from introduced B. tectorum populations have been previously reported as ranging from 0.241 to 0.582 (Bartlett et al., 2002; Novak & Mack, 2016; Novak et al., 1991; Valliant et al., 2007). However, within the native range of B. tectorum a G ST value of 0.754 has been reported, indicating greater population differentiation within the native range of B. tectorum compared to the introduced range (Novak & Mack, 1993). Within the small grain production fields of the PNW, G ST was calculated at 0.680 for accessions grouped by cluster, closer to the native range value and indicating a greater degree of population differentiation than what had previously been reported within the introduced range. Within its native range, downy brome exists as geographically isolated populations, while introduced populations typically consist of a mixture of several genotypes from unique founder events coexisting within a single location (Merrill et al., 2012; Scott et al., 2010). As this study used genotype to define clusters within a geographical region rather than defining populations by the geographical proximity of the sampling locations, greater population differentiation would be expected.

As the western PNW is considerably dryer and warmer than the eastern PNW, it was hypothesized that evidence of specialist genotypes would be found when comparing the eastern and western portions of the region. The small genetic partitioning values returned when comparing accessions by the agroecological class from which they were sourced indicates that land use class, which is predominately driven by climate, has limited influence on genetic partitioning. If strong genetic partitioning was found based upon the land class from which accessions were sourced, it would be evidence of specialist genotypes occupying specific niches based on climate or agronomic practices. Results do not suggest segregation of genotypes between the eastern and western portions of the region or by agroecological class. The lack of a strong or easy‐to‐interpret genetic cline may be an indication that climate is not a major driver of downy brome genotype distribution within the PNW. Downy brome might also be adaptable to a larger range of climates than represented within this study. Alternatively, the influence of climate might be more subtle than was detectable within this study.

The DAPC‐defined clusters describing downy brome genetic distribution were successful in identifying the ripgut brome individual. While some clusters contain greater numbers of individuals, it appears all clusters are distributed throughout the small grain production region and none of the genetic clusters can be described as specialist genotypes in relation to climatic variables or spatial distribution. Cluster distribution appears random, and a farm's location within the region is a poor indicator of what genotype(s) are likely to be found.

Compared to all other genetic clusters, cluster 3 appears to have a higher degree of genetic diversity when described by genetic distance (Figure 3) and expected heterozygosity (Table 4). Both of these measures were calculated utilizing all of the retained genetic markers after filtering SNPs for quality. When comparing the relation of cluster 3 to all other clusters described by the DAPC analysis, cluster 3 is also quite isolated from all other genetic clusters across the first linear discriminate (Figure 1a,b) but not across the second or third linear discriminant (Figure 1a–c). However, cluster 3 is not uniquely distributed across the study region, compared with other clusters (Figure 4). Therefore, although increased genetic diversity was reported, the diversity does not appear to be adaptively significant at a landscape scale.

Cluster 6, the ripgut brome outlier, is distinct from all other clusters across all linear discriminants used, and when comparing pairwise F ST values and genetic distance. The genetic distinction between other clusters is often slight, but genetic clusters can be separated based upon SNP distribution. Efforts were made to evaluate cluster membership with a different number of retained PCs or with arbitrarily selected k‐values, and those efforts failed to identify ripgut brome as an outlier. The results returned by DAPC may accurately reflect the state of downy brome population structure within the small grain production region of the PNW: an assemblage of inbred individuals with little evidence of outcrossing and varying degrees of shared genetic history, and without strong evidence of adaptation to differing environmental influences.

While genetic markers linked to neutral gene regions, and SNPs in particular, are well suited to neutral evolutionary process such as genetic drift and gene flow (Helyar et al. 2011), such genetic markers are poor at detecting active evolutionary processes (Narum et al., 2013). Previous studies have demonstrated neutral markers can fail to detect local adaptation of population to habitats (Narum et al., 2010; Storz et al., 2009). As previous literature has demonstrated flowering time as adaptively significant and influenced by local climate, the genes responsible for regulating flowering pathways are a promising target to investigate potential adaptation of downy brome to climate (Ball et al., 2004; Meyer et al., 2004; Rice & Mack, 1991a,b). Future work will look at the functions associated with discovered SNPs in conjunction with known genes associated with regulating phenology.

Research into the population genetics and structure of related species to down brome and species with similar life histories provides further context into the adaptation of selfing grass species to the selection imposed by agronomic settings. Green et al. (2001) compared diversity of the inbreeding annual or biennial weed Bromus sterilis L. (barren brome) between farms located in the United Kingdom. Barren brome exists as an assemblage of unique but inbred biotypes within agronomic fields. Similar to what was found from sampling B. tectorum within PNW small grain fields, considerable spatial mixing of genotypes was found across all sampled farm fields (Scott et al., 2010). When low genetic diversity was found within a field, Green et al. (2001) attributed diversity to selection of locally adapted inbred biotypes.

Population genetics and structure have also been investigated within Poaceae genus Setaria which contains several inbreeding summer annual agronomic weed species with worldwide distribution. Surveying genetic diversity and structure of Setaria viridis (L.) Beauv (Wang et al., 1995a,b). Wang et al. (1995a) reported a separation in genotypes between northern and southern groups within North America. However, at smaller geographical scales, including at the farm and state level, geographical patterns were difficult to detect with some areas exhibiting high degrees of population differentiation while others were genetically identical. Wang et al. (1995a) concluded that diversity within a region is likely a result of the number of independent introductions, and the intensity and duration of natural selection.

Wang et al. (1995b) expanded the analysis of Setaria species to S. glauca (L.) P. Beauv. S. geniculata P. Beauv. and S. faberi Herrm. Within the introduced range of the United States, S. geniculata and S. glauca both exhibited lower genetic diversity than what was found within their native range and regional patterns of genetic partitioning, while S. faberi was nearly genetically identical worldwide based upon the isozyme markers used. In summary Wang et al. (1995a,b) described the observed diversity of Setaria species in the context of a review of genetic diversity of 499 plant species conducted by Godt and Hamrick (1998). While significantly different from “average” plant species, the low genetic diversity and high population differentiation of both Setaria and Bromus species are typical of self‐pollinating, invasive grass species (Green et al., 2001; Novak et al., 1991).

Given the apparently low genetic diversity and the similar genetic structure of Bromus and Setaria species within invaded and agricultural land, high levels of genetic diversity may not be essential for colonizing species. However, the use within this study of neutral markers may have masked genetic diversity conferring local adaption to novel environments (Narum et al., 2010, 2013; Storz et al., 2009). Future work will look to identify nonneutral genetic markers, which may better describe the influence of climate and human management on distribution and evolution.

Within the small grain production region of the PNW, Bromus tectorum clusters are highly differentiated and randomly dispersed, suggesting that generalists rather than specialist genotypes predominated across the region. The current structure of diversity within the PNW is likely the result of several independent introductions, constrained by natural selection (Novak et al., 1991; Wang et al., 1995a,b). Given that a limited number of genetic clusters were found within the PNW, management strategies could be developed to target differences in phenotype between clusters. Previous studies have identified differences in Bromus tectorum herbicide susceptibility, germination characteristics, and date of seed production (Ball et al., 2004; Hulbert, 1955; Klemmedson & Smith, 1964; Lawrence, Burke, & Yenish, 2014). If traits exhibiting variation can be linked to genotype, management strategies can be developed to target the specific populations in a given field. This targeted weed management approach has been called for in the literature (Baucom & Holt, 2009) but has yet to be realized. However, clusters are likely intermixed at smaller spatial scales than surveyed in this study, which may limit the implementation of genotype‐specific management strategies as well mixed and distinct genotypes could adapt quickly to management strategies.

5. CONCLUSIONS

Analysis of population genetics and genetic structure from downy brome collected within an agronomic region indicates that the heterozygous state of downy brome is similar, if not marginally greater, to what has been reported in previous literature. Downy brome exists within the PNW small grain production region as a series of generalist genotype clusters with limited evidence of spatial adaptation, similar to what was reported Novak et al. (1991) in a broad survey of downy brome across North America. Given the apparent random spatial distribution of downy brome clusters at the spatial scale of this analysis, unique genotypes may be well mixed within small grain fields, similar to what was reported for Bromus sterilis (Green et al., 2001).

To expand further upon the current reported findings, future efforts should include more samples of individuals from the same field to increase the spatial resolution of genetic inferences. Additionally, collection of individuals from nearby rangeland and natural areas may allow for the control of climate and the comparison of land use among accessions. Finally, phenotyping of collected individuals in common garden studies across several years or locations would provide traits to be compared across individuals and elucidate the results of DAPC clustering by correlating the separation of genotypes with phenotyping.

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

DATA ACCESSIBILITY

Bioinformatic data are archived through the research funding agency website: https://data.reacchpna.org/resources/reacch-data-library/.

AUTHOR CONTRIBUTIONS

Nevin Lawrence contributed to study design, collection of plant materials, data analysis, and writing of the manuscript. Amber Hauvermale contributed to writing the manuscript. Amit Dhingra contributed to the data analysis and writing of this manuscript. Ian Burke contributed to the study design, collection of plant materials, data analysis, and manuscript composition and was the principle investigator.

ACKNOWLEDGMENTS

The authors wish to thank Dr. Deven See of the USDA‐ARS Western Regional Small Grains Genotyping Laboratory (WRSGGL) located in Pullman, WA, for providing access to their Ion Proton” semiconductor sequencer, and to Dr. Derick Jiwan for his assistance in running the Ion Proton” and preparing samples. The authors would also like to thank Dr. Doreen Main for access to software and servers utilized during analysis of bioinformatic data. This research was funded by a National Institute for Food and Agriculture competitive grant, Award Number: 2011‐68002‐30191.

Lawrence NC, Hauvermale AL, Dhingra A, Burke IC. Population structure and genetic diversity of Bromus tectorum within the small grain production region of the Pacific Northwest. Ecol Evol. 2017;7:8316–8328. https://doi.org/10.1002/ece3.3386

REFERENCES

  1. Ashley, M. C. , & Longland, W. S. (2009). Assessing cheatgrass (Bromus tectorum) genetic diversity and population structure using RAPD and microsatellite molecular markers. West North American Naturalist, 69, 63–74. [Google Scholar]
  2. Ball, D. A. , Frost, S. M. , & Gitelman, A. I. (2004). Predicting timing of downy brome (Bromus tectorum) seed production using growing degree days. Weed Science, 52, 518–524. [Google Scholar]
  3. Bartlett, E. , Novak, S. J. , & Mack, R. N. (2002). Genetic variation in Bromus tectorum (Poaceae): Differentiation in the eastern United States. American Journal of Botany, 89, 602–612. [DOI] [PubMed] [Google Scholar]
  4. Baucom, R. S. , & Holt, J. S. (2009). Weed of agricultural importance: Bridging the gap between evolutionary ecology and crop and weed science. New Phytologist, 184, 741–743. [DOI] [PubMed] [Google Scholar]
  5. Catchen, J. , Hohenlohe, P. A. , Bassham, S. , Amores, A. , & Cresko, W. A. (2013). Stacks: An analysis tool set for population genomics. Molecular Ecology, 22, 3124–3140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Elshire, R. J. , Glaubitz, J. C. , Sun, Q. , Poland, J. A. , Kawamoto, K. , Buckler, E. S. , & Mitchell, S. E. (2011). A robust, simple genotyping‐by‐sequencing (GBS) approach for high diversity species. PLoS ONE, 6, e19379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Godt, M. J. W. , & Hamrick, J. L. (1998). Allozyme diversity in the grasses In Cheplick G. P. (Ed.), Population biology of grasses (pp. 11–29). Cambridge, UK: Cambridge University Press. [Google Scholar]
  8. Green, J. M. , Barker, J. H. , Marshall, E. J. , Froud‐Williams, R. J. , Peters, N. C. B. , Arnold, G. M. , … Karp, P. (2001). Microsatellite analysis of the inbreeding grass weed Barren Brome (Anisantha sterilis) reveals genetic diversity at the within‐and between‐farm scales. Molecular Ecology, 10, 1035–1045. [DOI] [PubMed] [Google Scholar]
  9. Guillot, G. , Estoup, A. , Mortier, F. , & Cosson, J. F. (2005). A spatial statistical model for landscape genetics. Genetics, 170(3), 1261–1280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Guillot, G. , Leblois, R. , Coulon, A. , & Frantz, A. C. (2009). Statistical methods in spatial genetics. Molecular Ecology, 18, 4734–4756. [DOI] [PubMed] [Google Scholar]
  11. Hedrick, P. W. (2005). A standardized genetic differential measure. Evolution, 59, 1633–1638. [PubMed] [Google Scholar]
  12. Helyar, S. J. , Hemmer‐Hansen, J. , Bekkevold, D. , Taylor, M. I. , Ogden, R. , Limborg, M. T. , … Nielsen, E. E. (2011). Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Molecular Ecology Resources, 11, 123–136. [DOI] [PubMed] [Google Scholar]
  13. Huggins, D. , Rupp, R. , & Gessler, P. , et al. (2012). Dynamic Agroecological Zones for the Inland Pacific Northwest. American Society of Agronomy Annual Meeting. Oct. 21‐24, Cincinnati, OH, USA. [Google Scholar]
  14. Hulbert, L. (1955). Ecological studies of Bromus tectorum and other annual bromegrasses. Ecological Monographs, 25, 181–213. [Google Scholar]
  15. Jombart, T. (2008). Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics, 24, 1403–1405. [DOI] [PubMed] [Google Scholar]
  16. Jombart, T. , & Ahmed, I. (2011). Adegenet 1.3‐1: New tools for the analysis of genome‐wide SNP data. Bioinformatics, 27, 3070–3071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jombart, T. , Devillard, S. , & Balloux, F. (2010). Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genetics, 11, 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kao, R. H. , Brown, C. S. , & Hufbauer, R. A. (2008). High phenotypic and molecular variation in downy brome (Bromus tectorum). Invasive Plant Science and Management, 1(2), 216–225. [Google Scholar]
  19. Klemmedson, J. O. , & Smith, J. G. (1964). Cheatgrass (Bromus Tectorum L.). Botanical Review, 30, 226–262. [Google Scholar]
  20. Lawrence, N. C. , Burke, I. C. , & Yenish, J. P. (2014). 15 Years of Downy Brome Control in Eastern Washington. Proceedings of the 67th Annual Meeting of the WSWS, 12–13, Colorado Spring, CO: Western Society of Weed Science. [Google Scholar]
  21. Leger, E. A. , Espeland, E. K. , Merrill, K. R. , & Meyer, S. E. (2009). Genetic variation and local adaptation at a cheatgrass (Bromus tectorum) invasion edge in western Nevada. Molecular Ecology, 18, 4366–4379. [DOI] [PubMed] [Google Scholar]
  22. Manel, S. , Berthoud, F. , Bellemain, E. , Gaudeul, M. , Luikart, G. , Swenson, J. E. , Waits, L. P. , … Intrabiodiv Consortium (2007). A new individual‐based spatial approach for identifying genetic discontinuities in natural populations. Molecular Ecology, 16, 2031–2042. [DOI] [PubMed] [Google Scholar]
  23. Mascher, M. , Wu, S. , Amand, P. S. , Stein, N. , & Poland, J. (2013). Application of genotyping‐by‐sequencing on semiconductor sequencing platforms: A comparison of genetic and reference‐based marker ordering in barley. PLoS ONE, 8(10), e76925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Merrill, K. R. , Meyer, S. E. , & Coleman, C. E. (2012). Population genetic analysis of Bromus tectorum (Poaceae) indicates recent range expansion may be facilitated by specialist genotypes. American Journal of Botany, 99, 529–537. [DOI] [PubMed] [Google Scholar]
  25. Meyer, S. E. , Ghimire, S. , Decker, S. , Merrill, K. R. , & Coleman, G. E. (2013). The ghost of outcrossing past in downy brome, an inbreeding annual grass. Journal of Heredity, 104, 476–490. [DOI] [PubMed] [Google Scholar]
  26. Meyer, S. E. , Nelson, D. L. , & Carlson, S. L. (2004). Ecological genetics of vernalization response in Bromus tectorum L. (Poaceae). Annals of Botany, 93, 653–663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Narum, S. R. , Buerkle, C. A. , Davey, J. W. , Miller, M. R. , & Hohenlohe, P. A. (2013). Genotyping‐by‐sequencing in ecological and conservation genomics. Molecular Ecology, 22, 2841–2847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Narum, S. R. , Campbel, N. R. , Kozfkay, C. C. , & Meyer, K. A. (2010). Adaptation of redband trout in desert and montane environments. Molecular Ecology, 19, 4622–4637. [DOI] [PubMed] [Google Scholar]
  29. Nei, M. (1972). Genetic distances between populations. American Naturalist, 106, 283–292. [Google Scholar]
  30. Nei, M. (1973). Analysis of gene diversity in subdivided populations. Proceedings of the National Academy of Sciences of the United States of America, 70, 3321–3323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nei, M. (1978). Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics, 23, 341–369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Neve, P. , Vila‐Aoib, M. , & Roux, F. (2009). Evolutionary‐thinking in agriculture weed management. New Phytologist, 184, 783–793. [DOI] [PubMed] [Google Scholar]
  33. Novak, S. J. , & Mack, R. N. (1993). Genetic variation in Bromus tectorum introduced populations. Heredity, 71, 167–176. [Google Scholar]
  34. Novak, S. J. , & Mack, R. N. (2016). Mating systems, introduction and genetic diversity of Bromus tectorum in North America, the most notorious product of evolution within Bromus section Genea In Germino M. J., Chamber J. C., & Brown C. S. (Eds.), Exotic brome‐grasses in arid and semiarid ecosytems of the Western US: Causes, consequences, and management implications (pp. 99–132). Switzerland: Cham. [Google Scholar]
  35. Novak, S. , Mack, R. N. , & Soltis, D. (1991). Genetic variation in Bromus tectorum (Poaceae): Population differentiation in its North American range. American Journal of Botany, 78, 1150–1161. [Google Scholar]
  36. Novak, S. , Mack, R. N. , & Soltis, P. (1993). Genetic variation in Bromus tectorum (Poaceae): Introduction dynamics in North America. Canadian Journal of Botany, 71, 1441–1448. [Google Scholar]
  37. Paradis, E. , Claude, J. , & Strimmer, K. (2004). APE: Analyses of phylogenetics and evolution in R language. Bioinformatics, 20, 289–290. [DOI] [PubMed] [Google Scholar]
  38. Popescu, A. A. , Huber, K. T. , & Paradis, E. (2012). Ape 3.0: New tools for distance based phylogenetics and evolutionary analysis in R. Bioinformatics, 28, 1536–1537. [DOI] [PubMed] [Google Scholar]
  39. R Core Team (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; Retrieved from http://www.R-project.org/ [Google Scholar]
  40. Ramakrishnan, A. P. , Coleman, C. E. , Meyer, S. E. , & Fairbanks, D. J. (2002). Microsatellite markers for Bromus tectorum (cheatgrass). Molecular Ecology Notes, 2, 22–23. [Google Scholar]
  41. Ramakrishnan, A. P. , Meyer, S. E. , Fairbanks, D. J. , & Coleman, C. E. (2006). Ecological significance of microsatellite variation in western North American populations of Bromus tectorum . Plant Species Biology, 21, 61–73. [Google Scholar]
  42. Rice, K. , & Mack, R. N. (1991a). Ecological genetics of Bromus tectorum. I. A hierarchical analysis of phenotypic variation. Oecologia, 88, 77–83. [DOI] [PubMed] [Google Scholar]
  43. Rice, K. J. , & Mack, R. N. (1991b). Ecological genetics of Bromus tectorum. III. The demography of reciprocally sown populations. Oecologia, 88, 91–101. [DOI] [PubMed] [Google Scholar]
  44. Scott, J. W. , Meyer, S. E. , Merrill, K. R. , & Anderson, V. J. (2010). Local population differentiation in Bromus tectorum L. in relation to habitat‐specific selection regimes. Evolutionary Ecology, 24, 1061–1080. [Google Scholar]
  45. Storz, J. F. , Runck, A. M. , Sabatino, S. J. , Kelly, J. K. , Ferrand, N. , Moriyama, H. , Weber, R. E. , … Fago, A. (2009). Evolutionary and function insights into the mechanism underlying high‐altitude adaptation of deer mouse hemoglobin. Proceedings of the National Academy of Sciences of the United States of America, 106, 14450–14455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Strofer, A. , Murphy, M. A. , Evans, J. S. , Golberg, C. S. , Robinson, S. , Spear, S. F. , Dezzani, R. , … Waits, L. P. (2007). Putting the ‘landscape’ in landscape genetics. Heredity, 98, 128–142. [DOI] [PubMed] [Google Scholar]
  47. Thrall, P. H. , Oakeshott, J. G. , Fitt, G. , Southerton, S. , Burdon, J. J. , Sheppard, A. , Russell, R. J. , … Denison, R. F. (2011). Evolution in agriculture: The application of evolutionary approaches to the management of biotic interactions in agro‐ecosystems. Evolutionary Applications, 4(2), 200–215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Valliant, M. T. , Mack, R. N. , & Novak, S. J. (2007). Introduction history and population genetics of the invasive grass Bromus tectorum (Poaceae) in Canada Author. American Journal of Botany, 94, 1156–1169. [DOI] [PubMed] [Google Scholar]
  49. Wang, R. , Wendell, J. F. , & Dekker, J. H. (1995a). Weed adaptation in Setaria spp. I. Isozyme analysis of genetic diversity and population genetic structure in Setaria viridis . Botany‐Botanique, 82, 308–317. [Google Scholar]
  50. Wang, R. , Wendell, J. F. , & Dekker, J. H. (1995b). Weed adaptation in Setaria spp. I. Genetic diversity and population genetic structure in S. glauca, S. geniculata, and S. faberii (Poaceae). Botany‐Botanique, 82, 1031–1039. [Google Scholar]
  51. Ward, S. M. , & Jasieniuk, M. (2009). Review: Sampling weedy and invasive plant populations for genetic diversity. Weed Science, 57(6), 593–602. [Google Scholar]
  52. Winter, D. J. (2012). MMOD: An R library for the calculation of population differentiation statistics. Molecular Ecology Resources, 12, 1158–1160. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Bioinformatic data are archived through the research funding agency website: https://data.reacchpna.org/resources/reacch-data-library/.

Articles from Ecology and Evolution are provided here courtesy of Wiley

RESOURCES