1. Introduction
Land disturbances, species invasions, novel ecosystems, and climatic change are rapidly increasing worldwide. Because these challenges are both accelerating and difficult to reverse, a compelling need exists to develop plant materials that maintain native ecosystems, conserve natural resources, and deliver ecosystem services valued by humanity. For example, novel native plant materials may be advantageous when they offer the best opportunity for restoration success. This may occur when a restoration site has become altered beyond the local population’s ‘adaptive envelope’ [1], as when ecosystems have moved away from historical norms due to climate change [2,3], modified soils, or changes in vegetation [4,5]. Under such subpar conditions, plant populations that can tolerate augmented multiple stresses become more attractive for restoration relative to local populations that have evolved under different conditions.
Breed et al. [6] recently suggested the use of genomics to delineate plant provenances, to identify genetic adaptations for resilience, and to propagate novel genotypes through gene editing. The purpose of this synthesis paper is to illustrate how the emerging tools of genomic selection can be used to develop more effective native plant materials with key functional traits for ecosystem restoration. Genomic selection, based on recent dramatic advancements in molecular genetics technology, provides a more efficient path to plant material development than has been hitherto available. By employing genomic selection, it is possible to capitalize on genes controlling traits that are of value in target environments [7,8,9,10,11], leading to improvements in population performance.
1.1. Technical Advantages of Genomic Selection
Genomic technologies sufficient to scan the entire genome of cattle have been available for at least two decades [12,13], with great potential for many other domesticated crop, horticultural, and livestock species [14,15]. Selection can now be based on known genotypes based on discretely readable DNA sequences rather than on continuous phenotypic variables, which are usually confounded by environmental effects and experimental error. This has the potential for making selection more efficient and for making ecophysiological modeling of genotype–environment interactions more tractable [16]. Furthermore, the experimental unit of selection becomes more precise, moving from the whole-plant level to the gene level. Because diploid individuals display one (homozygous state) or two (heterozygous state) copies of a given allele, many replicates of a gene can be identified from across the population, increasing statistical power and experimental efficiency relative to clonal or seed replication [17]. Moreover, with enough genetic markers, another form of replication based on exact genetic similarities or kinships among individuals can be established, which is more accurate than traditional forms of replication based on pedigree relationships [18,19,20,21,22].
Genomic selection may dramatically improve the efficiency of selection relative to traditional methods. Using modern laboratory techniques and equipment, larger numbers of plants may be evaluated than would be previously possible at a fraction of the cost required for replicated field evaluations. For traits that are costly (e.g., C-isotope ratio), laborious (gas exchange), or inconvenient (persistence) to routinely phenotype, once DNA sequences associated with the trait are determined, the traits need not be remeasured. Seedlings may be screened and selected for traits expressed only in mature plants, reducing the length of time required to complete a cycle of selection. This has been invaluable for selection in long-lived trees [23,24]. However, it is also being applied to intermediate wheatgrass (Thinopyrum intermedium [Host] Barkw. & D.R. Dewey), a species being domesticated as a perennial grain crop [25] and to other perennial plants [14,26]. Exposure to multiple cycles of selection under contemporary changed-climate conditions may be advantageous for adaptation, especially for long-lived species that may have last recruited seedlings decades or centuries ago during an infrequent favorable climatic pulse that is no longer expected to occur [27,28].
1.2. Potential Advancements in Ecological Genetics
Besides the direct conservation benefits of more effective plant materials, we expect additional benefits for basic science [15,16]. This research can illuminate the intersection of plant ecology, physiology, and genetics through the prism of functional traits by elucidating how genes and genomes contribute to the traits that direct ecosystem processes. Several examples follow:
Adaptive traits can be genetically characterized and mapped to chromosome position. This is particularly advantageous for complex traits, i.e., those controlled by multiple loci and subject to environmental influence.
Allelic variants, i.e., alternative forms of the same gene, can be identified, allowing selection for alleles and quantification of their population frequencies.
A comprehensive genomic-based model may be developed. As more data are accumulated, the model may be improved, thereby reducing experimental error, increasing the precision of trait-mean estimates, and increasing heritability.
Trait combinations (plant syndromes) within or among populations can be identified and genetically characterized. Of particular interest are trait combinations that are adaptive under emerging environmental scenarios. Individuals or populations may be assigned to syndrome using multivariate analysis.
Development of isogenic trait-contrasting subpopulations is made possible through genomic selection or marker-assisted selection. Such comparisons can determine whether a given trait is favorable, neutral, or unfavorable at various levels of environmental variables and in various genetic backgrounds. This facilitates rigorous testing of hypotheses involving trait function and correlated trait responses.
2. Plant Material Performance
A critical need in dry-land revegetation is the development of native plant materials that are better adapted to a multiplicity of sites. They must be better able to establish, reproduce, and persist than those currently available. This may be accomplished by any combination of (1) adaptive genetic variation for functional traits that confer ecological fitness, (2) phenotypic plasticity that confers adaptation to the occupied environment, and (3) the ability to evolve in response to local environmental conditions, i.e., local adaptation.
2.1. Adaptive Genetic Variation
Identifying adaptive genetic variation requires testing plant material for performance [29,30,31]. Evaluation of plant material entails concentrated experimental effort. For some traits, greenhouse or transplanted field experiments may suffice for the trait in question. However, to test overall adaptation, seeded field trials are preferred, as seedling emergence, rather than post-emergence mortality, is typically the greatest limiting factor to obtaining an adequate stand [32]. Hereford [33] showed that adaptive trade-offs among environments are not strong enough to prohibit simultaneous adaptation to multiple environments. The desired genotype capitalizes on environmental conditions that favor it without being subject to severe negative trade-offs in alternate environments. Richards et al. [34] have referred to such genotypes as “jack-of-all-trades master-of-some”.
2.2. Phenotypic Plasticity
Identifying plant material with general adaptation across environments, conferred by phenotypic plasticity for adaptive traits, may allow species to colonize a diversity of environments [35,36]. When phenotypic plasticity enhances fitness, it may be considered adaptive [34]. High-plasticity species may function as ecological generalists, while adaptation of low-plasticity specialist species may be more limited [37]. While plasticity itself is subject to natural selection [34,35], it may inhibit evolution, as more-plastic entities are less impacted by natural selection [37].
2.3. Potential for Local Adaptation
Natural selection for local adaptation may be responsible for the observed ability of species to colonize disparate environments. For example, local adaptation may be present in invasive species that have only been present in their invasive range for relatively short periods. This suggests that local adaptation may be conferred by rapid evolution [38], particularly for self-incompatible species [39]. While much of the research on adaptation and plasticity has been conducted with invasive species, native species appear to follow the same assembly rules as invasive species [40]. Populations may develop de novo local adaptation via rapid evolution by exploiting high genetic variation [39,41,42], i.e., assisted evolution [4]. The phenomenon of rapid evolution for local adaptation may explain how invasive species are able to occupy a wide range of habitats [39]. Genetically augmented populations of native species may be useful for restoration applications [43], an approach termed admixture provenancing [44].
2.4. Issues Surrounding Selection
For cross-pollinated species such as bluebunch wheatgrass, genomic selection has the potential to address the elephant in the room, i.e., the inadequate performance of current native plant materials amid worsening environmental conditions [10]. As with any selection program, a potential concern is the possibility of narrowing the genetic base and resultant inbreeding. This involves two related issues. The first issue involves the alleles controlling the traits under selection. These alleles constitute a tiny portion of the entire genome, thus, such selection does not lead to highly inbred populations, though it could lead to a homozygous state at the affected loci. The second issue, that of reduced genetic variance and inbreeding, is a general consequence of either natural selection or artificial selection. Nevertheless, intentional steps may be taken to either minimize or reverse such losses in a selection program. Minimizing losses of genetic variation can be accomplished by maintaining a high effective population size (Ne), i.e., including many parents, in each cycle of selection [10]. Reversing losses may be accomplished by the introduction of immigrants, even when descended from the same base population [45]. This approach is effective because, while loss of genetic variation increases with intensity and occurrence of selection, it is a random stochastic process. Hence, loss of genetic variation under mild selection is unlikely to be for the same allele between any two populations selected from the same base. This means that an allele that randomly persists in one population may reverse the random loss of the same allele in another population upon introduction of individuals from the former population into the latter population. Finally, loss of genetic variation can be monitored and quantified with neutral genetic markers through the course of selection to determine whether corrective action is necessary.
3. Bluebunch Wheatgrass: A Restoration Workhorse Species
Bluebunch wheatgrass (Pseudoroegneria spicata [Pursh] À. Löve; BBWG) is a common perennial Triticeae bunchgrass species in sagebrush-steppe plant communities of the Intermountain West, USA (see Appendix A). Its distribution ranges from the eastern side of the Pacific Coastal Mountains to the western Great Plains and from the Yukon Territory to northern Mexico [46]. It is a climax species of wide ecological amplitude that commonly co-occurs with Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) on well-drained medium-textured soils [47,48]. Bluebunch wheatgrass is predominately diploid (2n = 14), but autotetraploid (2n = 28) populations occur in the northern portion of the species’ distribution [49]. This species is predominately cross-pollinating [50], making it an acceptable candidate for recurrent selection.
Bluebunch wheatgrass is the most widely used bunchgrass for land-rehabilitation seedings in the Intermountain West. As its seed supplies are plentiful and reasonable in cost, it is the best regional example of a native ‘workhorse’ species. Available plant materials include ‘Whitmar’ (released 1946), ‘Goldar’ (1989), P-7 Germplasm (2001), Anatone Germplasm (2004), and Columbia Germplasm (2015). In addition, geographical zones based on genetic similarity and traits that respond to natural selection have been identified for use in seed transfer guidelines [51,52,53].
However, available plant materials are still suboptimal for reliable performance in rangeland seedings, particularly on sites at the low end (<310 mm) of the average annual precipitation range where BBWG is found [30]. Despite acceptable establishment [31], BBWG displays lower productivity [54] and persistence [31] in comparison with introduced Triticeae wheatgrasses. This grass has suffered reduced abundance due to susceptibility to grazing and annual weed invasion [47]. Increased temperatures and drought may also favor downy brome (Bromus tectorum L.) as a competitor to BBWG [55]. Genomic selection applied to BBWG populations could be useful for ameliorating these issues that negatively impact its adaptation.
4. Bluebunch Wheatgrass: Plant Traits
We identified traits that may be important for any of five demographic stages (germination, emergence, seedling establishment, reproductive output, and persistence), as well as traits relating to developmental physiology (Table 1). We also associated each trait with one or more of six trait categories: (1) growth, (2) abiotic-stress response, (3) competitive-stress response, (4) defoliation-stress response, (5) reproductive output, and (6) persistence. To persist under current disturbance regimes and projected climate scenarios of the future, bunchgrasses must be able to establish quickly under cool temperatures, to consume excess soil resources to limit weed competition, to tolerate low soil moisture and defoliation, and to reproduce by seed and persist under wildland conditions.
4.1. Growth
Seed mass and seed density may have strong impacts on seed germination, seedling emergence, and seedling vigor. Across naturally occurring BBWG populations, seed mass has been positively correlated with both seedling emergence from a 4-cm seeding depth and seedling shoot dry-matter [88]. Populations with low seed mass germinated earlier and produced seedlings with greater specific root length and specific leaf area, while populations with high seed mass produced seedlings with higher initial biomass [60]. Hamerlynck et al. [61] investigated how the highly successful introduced bunchgrass, crested wheatgrass (Agropyron desertorum [Fisch. ex Link] J.A. Schult.), is better able to recruit seedlings and compete with downy brome relative to native perennial bunchgrasses like BBWG. They attributed these qualities to the high seed density of crested wheatgrass, double that of BBWG, which in turn has been attributed to high photosynthate production by the crested wheatgrass spike [61,89]. James et al. [32] examined survival of propagules across demographic stages during BBWG establishment in eastern Oregon, finding that most attrition of BBWG propagules occurred between germination and seedling emergence. This emergence bottleneck may relate to freezing and thawing of the seedbed, physical soil crusts, or susceptibility to pathogens.
4.2. Abiotic-Stress Response
Roots of BBWG have been shown to differ from those of downy brome and crested wheatgrass in their response to water. The higher root surface area of downy brome seedlings may account for their greater vigor relative to those of BBWG [74]. Compared to BBWG, downy brome seedlings are better able to avoid stress when infrequently watered, as evidenced by a less-negative xylem pressure potential, higher shoot water content, lower leaf temperature, and lower stomatal conductance [90]. Crested wheatgrass seedlings increased both shoot and root dry-matter in response to increased water availability, thereby maintaining high root mass fraction (root mass/[shoot + root mass]) [68]. In contrast, seedlings of BBWG responded to increased water by increasing shoot dry-matter while reducing root dry-matter in equal quantities, thereby lowering root mass fraction and forfeiting opportunities to capitalize on high soil water [68].
4.3. Competitive-Stress Response
Downy brome germinates more rapidly than BBWG, particularly under cooler temperatures [58,74,91,92]. This, coupled with opportunistic uptake of mineral nitrogen (i.e., NO3−–N and NH4+–N) during episodic high-temperature periods (>15 °C), may account for downy brome’s high nutrient-acquisition plasticity and invasiveness relative to native perennials like BBWG [36,93]. Walker et al. [94] suggested that an acquisitive strategy for nutrients may be beneficial for seedling growth, yet a nutrient conservation strategy may be paramount for longevity.
4.4. Defoliation-Stress Response
Bluebunch wheatgrass has long been regarded as highly susceptible to defoliation, particularly at the boot stage of phenological development [95]. This damage can be inhibited by removal of competitors [96], but it is accentuated by drought [97]. Following defoliation, both crested wheatgrass [98] and Snake River wheatgrass (Elymus wawawaiensis J. Carlson & Barkworth) [87,99] display superior regrowth relative to BBWG. Poor regrowth in response to defoliation is unrelated to carbohydrate reserves, as reserves in BBWG crowns were highest during the late boot stage [100], the same time when susceptibility to defoliation is greatest. In addition, regrowth was uncorrelated with carbohydrate concentration or pool size [101]. Both BBWG and crested wheatgrass seedlings show compensatory (increased) photosynthetic rate in response to defoliation [89,102]. However, in BBWG, this compensatory increase in photosynthesis reduced water use efficiency, while in crested wheatgrass, the reverse occurred. This suggests that BBWG has limited ability to cope with drying soil, especially during seedling establishment [81].
Instead of carbohydrate reserves, the superior regrowth of crested wheatgrass following defoliation relates to a relative lack of morphological constraints. Crested wheatgrass produced 17 times more daughter tillers than BBWG following defoliation [89] (Figure 8) and [101], despite similar numbers of meristematic buds [98]. In addition, crested wheatgrass allocates more resources to shoots and curtails root growth following defoliation, while BBWG roots grow unabated without apparent benefit to the plant [89]. Regrowth biomass in BBWG was correlated with tiller number but not mass per tiller [87]. Mukherjee et al. [103] reported that BBWG populations best able to compensate for defoliation were those with low undefoliated biomass, suggesting a trade-off between the above-ground productivity and defoliation tolerance.
4.5. Reproductive Output
Hamerlynck et al. [61] suggested that crested wheatgrass has traits that confer adaptation to a wide array of environmental conditions, enabling viable seed production in most years, while seed production of natives like BBWG is limited to years with wet spring conditions. Yield components of seed yield can be estimated through path-coefficient analysis. While no such analysis has been performed on BBWG, results are available for other perennial Triticeae species. Spikes per plant was the most important seed-yield component in Russian wildrye (Psathyrostachys juncea Nevski) [77], tall wheatgrass (Thinopyrum ponticum [Podp.] Z.-W. Liu & R.-C. Wang) [76], and western wheatgrass (Pascopyrum smithii Rydb.) [78], with seeds per spike and seed mass being much less important. However, all three yield components were important in basin wildrye (Leymus cinereus [Scribn. & Merr.] À. Löve) [79].
4.6. Persistence
Persistence of established BBWG plants is important because, while its persistence may be acceptable at sites with higher precipitation, it tends to be low on the drier sites that are the most difficult to establish. Ott et al. [104] evaluated results from 16-year-old 1999 seedings including Whitmar, Anatone, and Goldar BBWG at two proximal sites in west-central Utah. Cover increased over time at Mud Springs (368 mm average annual precipitation), but persistence was poor at Jericho (311 mm). Across 34 trials measured in the year following seeding and 22 trials measured three years post-seeding, Whitmar’s stand increased from 45 to 79%, while Anatone’s declined from 64 to 47% and Goldar’s declined from 63 to 37% [30]. These data corroborated the earlier findings of Hull [105], who evaluated 60 20-to-40-year-old seedings across southern Idaho. While BBWG performed ‘very poor’ overall, with many failures on the more arid sites, success was achieved with Whitmar in southwestern Idaho. Similar to Robins et al.’s [30] results, Stonecipher et al. [106] found that Goldar did not persist 12 years after seeding at Howell, UT (364 mm AAP) or Nephi, UT (375 mm). Bluebunch wheatgrass populations were similar to Siberian wheatgrass for persistence through year 5 at Cheyenne, WY (395 mm), Beaver, UT (337 mm), and Tintic, UT (372 mm) [31]. However, at Malta, ID, where precipitation is lower (270 mm), plant frequency decreased over time, with P-7 being best in year 1 and Goldar being worst in years 2–4. At year 5, BBWG stand was similar (p > 0.05) to that of ‘Vavilov II’ Siberian wheatgrass at Cheyenne, Beaver, and Tintic, but only 24% of Vavilov II at Malta. Collectively, these data suggest that Whitmar is more persistent than Goldar, and this difference is related to differences in long-term drought tolerance. Persistence is most problematic at drier sites, such as Malta, where stand loss is most severe and high-rainfall years are most infrequent. Thus, evaluations for persistence should take place at such sites.
5. Interfacing Ecological Filters and Genomics
Ecological filters represent dispersal, abiotic, biotic, or demographic processes that eliminate individuals during plant community assembly and may be controlled by management actions, e.g., seeding rates, burning, fertilization, tillage, and litter management [107]. For example, Grman et al. [108] compared the relative strength of dispersal and establishment filters for 39 species across 29 tallgrass prairie restoration plantings in southwestern Michigan by varying seeding rates and measuring soil parameters, precipitation, and plant occurrence.
Here we interface (1) an ecological trait model incorporating critical ecological traits screened by abiotic/biotic and demographic ecological filters (Figure 1, Table 1) with (2) a genomic trait model that can make phenotypic predictions based on genomic DNA sequences (genotypes). This permits direct selection for genes that control the ecological traits that facilitate filter penetration (Figure 2). However, these two models can be expected to require modification with continued selection due to changing gene frequencies. Thus, ‘retuning’ the genomic model may be required (feedback arrow on right-hand side of Figure 2). Trait assessment (middle of Figure 2) may also require retuning (feedback arrow on left-hand side of Figure 2), resulting in a modified ecological trait model.
6. Genomics and Selection
A genomic approach may facilitate the development of the plant materials needed to restore degraded ecosystems across a broad geographic scale [6,109]. Genomic tools have many recognized applications in biological conservation and ecological restoration that are based on the analysis of neutral and adaptive genetic variation [6]. Some of these tools have been used to elucidate genetic diversity and genetic history of BBWG populations used in restoration [51,53,110]. However, marker assisted selection (MAS; see Appendix B), and more recently genomic selection (GS), also have underutilized potential to identify favorable genetic combinations [109,111,112,113] for restoration of natural ecosystems. They can be used to construct a framework to link genome sequences with environmentally modulated plant responses [16]. Until recently, these technologies have not been utilized to develop plant materials for restoration and for enhancing ecosystem services. However, rapid proliferation of ‘next-generation’ DNA sequencing technologies over the past two decades now permits development of robust genomic resources beyond a small number of crop and livestock species [12,114,115].
6.1. DNA-Based Selection
The genomic technologies of MAS and GS enable the evaluation and selection of natural genetic variation at the DNA level. Traditional processes of evaluation, selection, and intermating of individuals are still required, but MAS and GS are based on relatively simple DNA markers (genotypes) instead of plant phenotypes, which are easily confounded by environmental effects. Markers may be single-nucleotide polymorphisms (SNPs) or small DNA insertions or deletions (indels) that usually have only two alternative forms. These genomic tools enable scientists to overcome inherent challenges to the adequate evaluation of complex traits, i.e., those subject to a mixture of complex genetic and confounding non-genetic effects in genetically heterogeneous populations.
In the past, extensive replication of field studies across a variety of locations has been required to accurately characterize complex traits [116]. For self-pollinating species, such as many cereal crops, requirements for experimental replication can be satisfied by using seed of inbred lines. However, most perennial plant species, including BBWG, are allogamous (cross-pollinating) and self-incompatible, resulting in genetically diverse individuals. In these cases, replication can be facilitated by clonal propagation, but a more efficient statistical alternative is to determine breeding values using best linear unbiased prediction (BLUP). A BLUP value is based not only on the performance of the individual, but also on the performance of its relatives weighted by their genetic relationship, as approximated by pedigree information [117,118,119]. Genomic technologies provide the exact genetic relatedness among individuals, rather than the expected value based on pedigree.
6.2. Genomic Selection
The main goal of GS is to develop models to accurately predict breeding values, i.e., genome-estimated breeding values (GEBVs), of individuals using DNA-sequence or DNA-marker information. In contrast to MAS (see Appendix B), the more generalized GS approach utilizes many DNA markers with sufficient marker density to detect and select favorable DNA linkages. The accuracy of GEBVs is a function of the number of markers and the size of the training population, i.e., the number of individuals with known genotypes and phenotypes based on training records. Because the number of markers typically exceeds the size of the training population, thereby creating an estimation problem, special statistical methods are utilized to address this issue (see Appendix C).
Notwithstanding technical and mathematical complexities, the potential acceleration of breeding efficiencies enabled by GS is impressive [12,118,120,121]. The rapid proliferation of genomic resources opens new avenues for application of MAS and GS for restoration of native plant species threatened by introduced pathogens or native pathogens that are increasing with climate change [122]. For example, genetic linkage maps and a draft genome reference sequence for the American chestnut will facilitate the application of MAS and GS to select for resistance to an alien blight fungus (Cryophonectria parasitica) in this keystone tree species [109,112,123,124].
Although breeding has the potential to facilitate restoration of wild plants and animals, any potential loss of genetic diversity due to breeding is a concern [125,126], especially for rare or endangered species where genetic diversity has already been restricted [127,128]. The loss of genetic diversity is always a concern in breeding because the gains from selection are closely related to the amount of heritable genetic variation [129,130]. Compared to pedigree-based phenotypic selection, genomic selection may result in a more rapid decline in genetic variation and selection response [131,132]. However, it has also been shown that genomic selection can maintain higher levels of diversity because it measures genetic relatedness more accurately [129,133,134,135]. One particular problem with traditional phenotypic selection in self-incompatible, wind-pollinated grasses is that the pollen parents are usually unknown and the success of pollen parents is non-random [115]. Paternity analysis using genomic DNA markers has shown that some pollen parents are much more successful than others, which reduces the effective population size if genomic relationships are not controlled in the selection process [115]. Methods of genomic selection are now being developed to both maximize genetic gains and maintain the high levels of genetic variation needed to maintain future selection response [133,136,137]. These methods have potential applications for improving restoration outcomes.
7. Conclusions
We believe an ecological genomics model, coupled with quantitative assessment of plant functional traits, will provide a new essential toolkit for arid land restoration efforts. The most powerful aspect of this approach is that it may predict plant performance across the full range of testable demographic processes. Such efforts are critical in face of the ongoing increase in regional climate variability associated with global climate change, which will likely accelerate rangeland degradation. Indeed, Kilkenny’s [138] climate model has predicted that BBWG is vulnerable to extirpation in the Snake River Plain and Columbia Plateau due to warming temperatures. Whitham [3] lamented the lack of good plant-material options for populations compromised at the warm end of species’ distributions. Furthermore, seed-transfer options may be limited due to the emergence of no-analog ecosystems as a response to climate change [139,140,141,142]. A justification for developing more effective native plant materials can be made on this basis. While in theory, natural selection will eventually produce well-adapted native species, existing and pending ecological and economic consequences require more immediate action to address the huge areal extent of this problem.
Conceptualization, T.A.J., T.A.M. and S.R.L.; Writing—Original Draft Preparation, T.A.J., T.A.M. and S.R.L.; Writing—Review & Editing, T.A.J., T.A.M., S.R.L., E.P.H. and J.L.C. All authors have read and agreed to the published version of the manuscript.
The authors wish to thank Kevin B. Jensen and other anonymous reviewers for constructive reviews of the early drafts of the manuscript.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figure 1. Sorting of undesirable from desirable phenotypes in the seeded genetic pool through selection for seedling establishment (modulated primarily by a biotic filter) and seedling persistence (modulated primarily by an abiotic filter), ultimately resulting in the genetic pool present in the plant community.
Figure 2. A flowchart for development of plant materials and their subsequent delivery to the marketplace.
Demographic filters and associated traits.
Demographic Filters/Traits | Trait Category 1 | Units | References |
---|---|---|---|
Germination | |||
rate at room temperature | 1 | index value | [ |
rate at 10 °C | 1, 2 | index value | [ |
seed mass | 1 | mg seed−1 | [ |
Emergence | |||
seed mass | 1, 2 | mg seed−1 | [ |
seed density | 1, 2 | mg cc−1 | [ |
deep-seeding (5 cm) emergence | 2 | % | [ |
emergence rate (10 °C) | 1, 2 | index value | [ |
coleoptile density | 2 | dry weight per unit fresh weight | [ |
elongated subcoleoptile internode | 2 | frequency (%), length (mm) | [ |
Seedling establishment | |||
cold-temperature (10 °C) growth | 1, 2, 3 | mg seedling−1 | [ |
root biomass | 1, 2, 3 | mg seedling−1 | [ |
root:shoot biomass | 1, 2, 3 | mg mg−1 | [ |
root length | 2, 3 | cm seedling−1 | [ |
specific root length | 2, 3 | cm g−1 | [ |
root tips | 3 | number seedling−1 | [ |
tiller recruitment (number and stage) | 1, 4 | tillers seedling−1 stage−1 | [ |
specific leaf area | 1, 2, 3 | cm2 g−1 | [ |
recovery from 70% defoliation | 4 | mg regrowth seedling−1 | [ |
Reproductive output | |||
spike number | 5 | spikes plant−1 | [ |
seeds per spike | 5 | seeds spike−1 | [ |
seed mass | mg seed−1 | [ |
|
Persistence | |||
change in stand | 6 | % (year 1 vs. year 3, year 5) | [ |
change in spike number | 6 | spikes plant−1 (year 2 vs. year 5) | |
Developmental physiology | |||
shoot biomass | 1 | G | [ |
root biomass | 1, 2 | G | [ |
root mass fraction | 1, 2, 3 | g g−1 | [ |
height | 1, 3 | cm | [ |
dark respiration | 1 | mmol mol m−2 s−1 | [ |
net assimilation rate | 1 | mmol mol m−2 s−1 | [ |
carboxylation efficiency | 1 | mmol mol m−2 s−1 | [ |
instantaneous water-use efficiency | 2, 3 | net assimilation rate per unit of stomatal conductance | [ |
water-use efficiency (integrated over time) measured by carbon-isotope discrimination | 2 | δ 13C/12C (%) | [ |
recovery from 10-cm defoliation | 4 | spikes plant−1 | [ |
1 Trait categories are (1) growth, (2) abiotic-stress response, (3) competitive-stress response, (4) defoliation-stress response, (5) reproductive output, and (6) persistence.
Appendix A. Restoration Urgency in Sagebrush-Steppe Plant Communities
A major challenge in ecosystem restoration and conservation is maintaining viable and diverse plant populations and communities [
Losses to ecosystem functioning after broadscale and persistent bunchgrass decline following European settlement of the Intermountain West are well documented [
Establishing viable bunchgrass populations is the most economical and effective method to halt and reverse invasive annual grass spread and restore sagebrush-steppe. However, success rates are low (ca. 3%), being limited by highly variable interannual precipitation, low seedling emergence, and establishment success, especially for native bunchgrass species [
Establishing resilient native bunchgrasses is also an effective method to promote beneficial ecosystem services. Native bunchgrasses produce deep roots and stabilize soils [
Appendix B. Marker-Assisted Selection Versus Genomic Selection
Early use of DNA markers in tomato enabled scientists to detect alleles of at least 10 quantitative trait loci (QTLs) controlling inheritance of fruit size, soluble solids, and pH using 70 genetic markers in a family of 237 progeny derived from a backcross of two divergent strains [
Both MAS and GS can be used to improve populations, but they differ in terms of training populations, DNA markers, statistical methods, and breeding program goals. The older MAS approach allows researchers to identify, track, and select specific genes or QTLs using a few DNA markers beginning with an experimental training population. The newer, more comprehensive GS approach enables researchers to identify and select numerous DNA markers associated with complex arrays of multi-allelic effects in highly heterogeneous populations. Unlike MAS, GS does not prioritize a small set of DNA markers targeting genes or QTLs with large effects, but rather it utilizes a sufficiently large number of markers to capture the diversity of alleles across all known and unknown (or undefined) QTLs. Using GS models developed from a training population, extrapolation can then be made to somewhat distantly related populations, which is less feasible in MAS.
Both approaches require an experimental training population that is genotyped and phenotyped to identify associations between DNA markers and traits. The MAS approach enables researchers to track and select marker-trait associations initially identified in the training population. This enables selection of individuals with desirable alleles in the training population and their introgression into the target population. In contrast, the GS approach uses a statistical model developed from the training population to predict phenotypes (breeding values) of individuals of a predicted population based solely on genotype. Where MAS is usually focused on one or a few genes or QTLs, GS is ultimately focused on breeding values based on the entire genome.
The main goal of MAS is to identify one or more DNA markers that can be used to select large-effect genes or QTLs. For MAS, the training population is typically a genetically well-defined population, such as a full-sib family where all individuals have the same two parents. In such a population, linkage disequilibrium (LD) between widely spaced markers and genes is relatively high as a result of the physical linkage of different alleles on different copies of each chromosome in each parent [
Statistical analysis is typically a somewhat complicated two-step process. First, genetic linkage maps of the DNA markers must be constructed. This is based, in part, on the non-random assortment of markers located on the same chromosome, as well as the relationship between genetic recombination and physical distances between markers within a chromosome pair. Distances are measured in Morgans (M) or centiMorgans (cM), units of chromosome length named after the pioneering Drosophila geneticist, Thomas Hunt Morgan. The total M of an organism’s genome roughly corresponds to the haploid chromosome number of that organism. Second, the analysis usually involves some form of interval QTL analysis based on marker genotypes and the inferred genotypes in the chromosomal interval between markers [
Appendix C. Statistical Methods for Estimating Genome-Estimated Breeding Values
Improvements to the accuracy of genome-estimated breeding values (GEBVs), i.e., predicted breeding values for individuals not in the training population, can be achieved with up to 10 NeL markers and at least 1 NeL individuals in the training population, where Ne is the effective population size and L is the length of the genome in Morgans [
To rectify this problem, regression coefficients for each marker are shrunk or eliminated using ridge regression (RR-BLUP), least absolute shrinkage and selection operator (LASSO) regression, or elastic net regression [
References
1. Johnson, R.; Stritch, L.; Olwell, P.; Lambert, S.; Horning, M.E.; Cronn, R. What are the best seed sources for ecosystem restoration on BLM and USFS lands?. Nativ. Plants J.; 2010; 11, pp. 117-131. [DOI: https://dx.doi.org/10.2979/NPJ.2010.11.2.117]
2. Havens, K.; Vitt, P.; Stille, S.; Kramer, A.T.; Fant, J.B.; Schatz, K. Seed sourcing for restoration in an era of climate change. Nat. Areas J.; 2015; 35, pp. 122-133. [DOI: https://dx.doi.org/10.3375/043.035.0116]
3. Whitham, T.G.; Gehring, C.A.; Bothwell, H.M.; Cooper, H.F.; Hull, J.B.; Allan, G.J.; Grad, K.C.; Markovchick, L.; Shuster, S.M.; Parker, J. et al. Using the Southwest Experimental Garden Array to enhance riparian restoration in response to global environmental change: Identifying and deploying genotypes and populations for current and future environments. Riparian Research and Management: Past, Present, Future; Carothers, S.W.; Johnson, R.R.; Finch, D.M.; Kingsley, K.J.; Hamre, R.H. USDA Forest Service: Washington, DC, USA, 2020; RMRS GTR-411; Volume 2, pp. 63-79.
4. Jones, T.A.; Monaco, T.A. A role for assisted evolution in designing native plant materials. Front. Ecol. Environ.; 2009; 7, pp. 541-547. [DOI: https://dx.doi.org/10.1890/080028]
5. Jones, T.A.; Monaco, T.A.; Rigby, C.W. The potential of novel native plant materials for the restoration of novel ecosystems. Elementa; 2015; 3, 47. [DOI: https://dx.doi.org/10.12952/journal.elementa.000047]
6. Breed, M.F.; Harrison, P.A.; Blyth, C.; Byrne, M.; Gaget, V.; Gellie, N.J.C.; Groom, S.V.C.; Hodgson, R.; Mills, J.G.; Prowse, T.A.A. et al. The potential of genomics for restoring ecosystems and biodiversity. Nat. Rev. Genet.; 2019; 20, pp. 615-628. [DOI: https://dx.doi.org/10.1038/s41576-019-0152-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31300751]
7. Thomas, M.A.; Klaper, R. Genomics for the ecological toolbox. Trends Ecol. Evol.; 2004; 19, pp. 439-445. [DOI: https://dx.doi.org/10.1016/j.tree.2004.06.010]
8. DeHaan, L.R.; Van Tassel, D.L.; Cox, T.S. Perennial grain crops: A synthesis of ecology and plant breeding. Renew. Agric. Food Syst.; 2007; 20, pp. 5-14. [DOI: https://dx.doi.org/10.1079/RAF200496]
9. Brummer, E.C.; Barber, W.T.; Collier, S.M.; Cox, T.S.; Johnson, R.; Murray, S.C.; Olsen, R.T.; Pratt, R.C.; Thro, A.M. Plant breeding for harmony between agriculture and the environment. Front. Ecol. Environ.; 2011; 9, pp. 561-568. [DOI: https://dx.doi.org/10.1890/100225]
10. Chivers, I.H.; Jones, T.A.; Broadhurst, L.M.; Mott, I.W.; Larson, S.R. The merits of artificial selection for the development of restoration-ready plant materials of native perennial grasses. Restor. Ecol.; 2016; 24, pp. 174-183. [DOI: https://dx.doi.org/10.1111/rec.12323]
11. Ryan, M.R.; Crews, T.E.; Culman, S.W.; DeHaan, L.R.; Hayes, R.C.; Jungers, J.M.; Baker, M.G. Managing for multifunctionality in perennial grain crops. Bioscience; 2018; 68, pp. 294-304. [DOI: https://dx.doi.org/10.1093/biosci/biy014]
12. Meuwissen, T.H.E.; Hayes, B.J.; Goddard, M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics; 2001; 157, pp. 1819-1829. [DOI: https://dx.doi.org/10.1093/genetics/157.4.1819] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11290733]
13. Meuwissen, T.; Hayes, B.; Goddard, M. Accelerating improvement of livestock with genomic selection. Ann. Rev. Anim. Biosci.; 2013; 1, pp. 221-237. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25387018][DOI: https://dx.doi.org/10.1146/annurev-animal-031412-103705]
14. Lin, Z.; Hayes, B.J.; Daetwyler, H.D. Genomic selection in crops, trees and forages: A review. Crop Pasture Sci.; 2014; 65, pp. 1177-1191. [DOI: https://dx.doi.org/10.1071/CP13363]
15. Hickey, J.M.; Chiurugwi, T.; Mackay, I.; Powell, W. Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat. Genet.; 2017; 49, pp. 1297-1303. [DOI: https://dx.doi.org/10.1038/ng.3920]
16. Wang, D.R.; Guadagno, D.R.; Mau, X.; Mackay, D.S.; Pleban, J.R.; Baker, R.L.; Weinig, C.; Jannink, J.-L.; Ewers, B.E. A framework for genomics-informed ecophysiological modeling in plants. J. Exper. Bot.; 2019; 70, pp. 2561-2574. [DOI: https://dx.doi.org/10.1093/jxb/erz090]
17. Hayes, B.; Goddard, M. Genome-wide association and genomic selection in animal breeding. Genome; 2010; 53, pp. 876-883. [DOI: https://dx.doi.org/10.1139/G10-076] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21076503]
18. Zhang, Z.; Todhunter, R.J.; Buckler, E.S.; Van Vleck, L.D. Technical note: Use of marker-based relationships with multiple-trait derivative-free restricted maximal likelihood. J. Anim. Sci.; 2007; 85, pp. 881-885. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17085728][DOI: https://dx.doi.org/10.2527/jas.2006-656]
19. VanRaden, P.M. Efficient methods to compute genomic predictions. J. Dairy Sci.; 2008; 91, pp. 4414-4423. [DOI: https://dx.doi.org/10.3168/jds.2007-0980]
20. Hayes, B.J.; Visscher, P.M.; Goddard, M.E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res.; 2009; 91, pp. 47-60. [DOI: https://dx.doi.org/10.1017/S0016672308009981]
21. Piepho, H.P. Ridge regression and extensions for genomewide selection in maize. Crop Sci.; 2009; 49, pp. 1165-1176. [DOI: https://dx.doi.org/10.2135/cropsci2008.10.0595]
22. Isik, F.; Holland, J.; Maltecca, C. Genomic relationships and GBLUP. Genetic Data Analysis for Plant and Animal Breeding; Isik, F.; Holland, J.; Maltecca, C. Springer International Publishing: Cham, Germany, 2017; pp. 311-354.
23. Harfouche, A.; Meilan, R.; Kirst, M.; Morgante, M.; Boerjan, W.; Sabatti, M.; Scarascia Mugnozza, G. Accelerating the domestication of forest trees in a changing world. Trends Plant Sci.; 2012; 17, pp. 64-72. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22209522][DOI: https://dx.doi.org/10.1016/j.tplants.2011.11.005]
24. Kumar, S.; Hilario, E.; Deng, C.H.; Molloy, C. Turbocharging introgression breeding of perennial fruit crops: A case study on apple. Hort. Res.; 2020; 7, 47. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32257233][DOI: https://dx.doi.org/10.1038/s41438-020-0270-z]
25. Crain, J.; Bajgain, P.; Anderson, J.; Zhang, X.; DeHaan, L.; Poland, J. Enhancing crop domestication through genomic selection, a case study of intermediate wheatgrass. Front. Plant Sci.; 2020; 11, 319. [DOI: https://dx.doi.org/10.3389/fpls.2020.00319] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32265968]
26. Hayes, B.J.; Cogan, N.O.I.; Pembleton, L.W.; Goddard, M.E.; Wang, J.P.; Spangenberg, G.C.; Forster, J.W. Prospects for genomic selection in forage plant species. Plant Breed.; 2013; 132, pp. 133-143. [DOI: https://dx.doi.org/10.1111/pbr.12037]
27. Schwinning, S.; Sala, O.E.; Loik, M.E.; Ehleringer, J.R. Thresholds, memory, and seasonality: Understanding pulse dynamics in arid/semi-arid ecosystems. Oecologia; 2004; 141, pp. 191-193. [DOI: https://dx.doi.org/10.1007/s00442-004-1683-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15300489]
28. Hourihan, E.; Schultz, B.W.; Perryman, B.L. Climatic influences on establishment pulses of four Artemisia species in Nevada. Rangel. Ecol. Manag.; 2018; 71, pp. 77-86. [DOI: https://dx.doi.org/10.1016/j.rama.2017.08.002]
29. Asay, K.H.; Horton, W.H.; Jensen, K.B.; Palazzo, A.J. Merits of native and introduced Triticeae grasses on semiarid rangelands. Can. J. Plant Sci.; 2001; 81, pp. 45-52. [DOI: https://dx.doi.org/10.4141/P99-131]
30. Robins, J.G.; Jensen, K.B.; Jones, T.A.; Waldron, B.L.; Peel, M.D.; Rigby, C.W.; Vogel, K.P.; Mitchell, R.E.; Palazzo, A.J.; Cary, T.J. Stand establishment and persistence of perennial cool-season grasses in the Intermountain West and the Central and Northern Great Plains. Rangel. Ecol. Manag.; 2013; 66, pp. 181-190. [DOI: https://dx.doi.org/10.2111/REM-D-11-00022.1]
31. Rigby, C.W.; Jensen, K.B.; Creech, J.E.; Thacker, E.T.; Waldron, B.L.; Derner, J.D. Establishment and trends in persistence of selected perennial cool-season grasses in western United States. Rangel. Ecol. Manag.; 2018; 71, pp. 681-690. [DOI: https://dx.doi.org/10.1016/j.rama.2018.06.008]
32. James, J.J.; Svejcar, T.J.; Rinella, M.J. Demographic processes limiting seedling recruitment in arid grassland restoration. J. Appl. Ecol.; 2011; 48, pp. 961-969. [DOI: https://dx.doi.org/10.1111/j.1365-2664.2011.02009.x]
33. Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Nat.; 2009; 173, pp. 579-588. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19272016][DOI: https://dx.doi.org/10.1086/597611]
34. Richards, C.L.; Bossdorf, O.; Muth, N.Z.; Gurevitch, J.; Pigglucci, M. Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecol. Lett.; 2006; 9, pp. 981-993. [DOI: https://dx.doi.org/10.1111/j.1461-0248.2006.00950.x]
35. Nicotra, A.B.; Davidson, A. Adaptive phenotypic plasticity and plant water use. Funct. Plant Biol.; 2010; 37, pp. 117-127. [DOI: https://dx.doi.org/10.1071/FP09139]
36. Leffler, A.J.; Monaco, T.A.; James, J.J. Nitrogen acquisition by annual and perennial grass seedlings: Testing the roles of performance and plasticity to explain plant invasion. Plant Ecol.; 2011; 212, pp. 1601-1611. [DOI: https://dx.doi.org/10.1007/s11258-011-9933-z]
37. Sultan, S.E. Phenotypic plasticity for plant development, function and life history. Trends Plant Sci.; 2002; 5, pp. 537-542. [DOI: https://dx.doi.org/10.1016/S1360-1385(00)01797-0]
38. Dlugosch, K.M.; Parker, I.M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol.; 2008; 17, pp. 431-449. [DOI: https://dx.doi.org/10.1111/j.1365-294X.2007.03538.x]
39. Oduor, A.M.L.; Leimu, R.; van Kleunen, M. Invasive plant species are locally adapted just as frequently and at least as strongly as native plant species. J. Ecol.; 2016; 104, pp. 957-968. [DOI: https://dx.doi.org/10.1111/1365-2745.12578]
40. Lemoine, N.P.; Burkepile, D.E.; Parker, J.D. Quantifying differences between native and introduced species. Trends Ecol. Evol.; 2016; 31, pp. 372-381. [DOI: https://dx.doi.org/10.1016/j.tree.2016.02.008]
41. Kinnison, M.T.; Unwin, M.J.; Quinn, T.P. Eco-evolutionary vs. habitat contributions to invasion in salmon: Experimental evaluation in the wild. Mol. Ecol.; 2008; 17, pp. 405-414. [DOI: https://dx.doi.org/10.1111/j.1365-294X.2007.03495.x]
42. Reusch, T.B.H.; Wood, T.E. Molecular ecology of global change. Mol. Ecol.; 2007; 16, pp. 3973-3992. [DOI: https://dx.doi.org/10.1111/j.1365-294X.2007.03454.x]
43. Jones, T.A. The Restoration Gene Pool concept: Beyond the native versus non-native debate. Restor. Ecol.; 2003; 11, pp. 281-290. [DOI: https://dx.doi.org/10.1046/j.1526-100X.2003.00064.x]
44. Breed, M.F.; Stead, M.G.; Ottewell, K.M.; Gardner, M.G.; Lowe, A.J. Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conserv. Genet.; 2013; 14, pp. 1-10. [DOI: https://dx.doi.org/10.1007/s10592-012-0425-z]
45. Swindell, W.R.; Bouzat, J.L. Gene flow and adaptive potential in Drosophila melanogaster. Conserv. Genet.; 2006; 7, pp. 79-89. [DOI: https://dx.doi.org/10.1007/s10592-005-8223-5]
46. Carlson, J.R. Pseudoroegneria (Nevski) Á. Löve. Flora of North America; Flora of North America Editorial Committee. Oxford University Press: New York, NY, USA, 2007; Volume 24, pp. 279-283.
47. Miller, R.F.; Seufert, J.M.; Haferkamp, M.R. The ecology and management of bluebunch wheatgrass (Agropyron spicatum): A review. Oregon Agricultural Experiment Station Bulletin 669; Oregon State University: Corvallis, OR, USA, 1986.
48. Wilder, L.E.; Veblen, K.E.; Schupp, E.W.; Monaco, T.A. Seedling emergence patterns of six restoration species in soils from two big sagebrush plant communities. West. N. Am. Nat.; 2019; 79, pp. 233-246. [DOI: https://dx.doi.org/10.3398/064.079.0209]
49. Gibson, A.; Nelson, C.R. Comparing provisional seed transfer zone strategies for a commonly seeded grass, Pseudoroegneria spicata. Nat. Areas J.; 2017; 37, pp. 188-199. [DOI: https://dx.doi.org/10.3375/043.037.0208]
50. Jensen, K.B.; Zhang, Y.F.; Dewey, D.R. Mode of pollination of perennial species of the Triticeae in relation to genomically defined genera. Can. J. Plant Sci.; 1990; 70, pp. 215-225. [DOI: https://dx.doi.org/10.4141/cjps90-024]
51. Larson, S.R.; Jones, T.A.; Jensen, K.B. Population structure in Pseudoroegneria spicata (Poaceae: Triticeae) modeled by Bayesian clustering of AFLP genotypes. Am. J. Bot.; 2004; 91, pp. 1789-1801. [DOI: https://dx.doi.org/10.3732/ajb.91.11.1789] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21652326]
52. St. Clair, J.B.; Kilkenny, F.F.; Johnson, R.C.; Shaw, N.L.; Weaver, G. Genetic variation in adaptive traits and seed transfer zones for Pseudoroegneria spicata (bluebunch wheatgrass) in the northwestern United States. Evol. Appl.; 2013; 6, pp. 933-948. [DOI: https://dx.doi.org/10.1111/eva.12077] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24062802]
53. Massatti, R.; Prendeville, H.R.; Larson, S.; Richardson, B.A.; Waldron, B.L.; Kilkenny, F.F. Population history provides foundational knowledge for utilizing and developing native plant restoration materials. Evol. Appl.; 2018; 11, pp. 2025-2039. [DOI: https://dx.doi.org/10.1111/eva.12704]
54. Robins, J.G.; Waldron, B.L.; Jensen, K.B. Productivity, stability, and resilience of cool-season perennial grasses used for rangeland rehabilitation. Agroecosyst. Geosci. Environ.; 2020; 3, e20002.
55. Larson, C.D.; Lehnhoff, E.A.; Noffsinger, C.; Rew, L.J. Competition between cheatgrass and bluebunch wheatgrass is altered by temperature, resource availability, and atmospheric CO2 concentration. Oecologia; 2018; 186, pp. 855-868. [DOI: https://dx.doi.org/10.1007/s00442-017-4046-6]
56. Maguire, J.D. Speed of germination—Aid in selection and evaluation for seedling emergence and vigor. Crop Sci.; 1962; 2, pp. 176-177. [DOI: https://dx.doi.org/10.2135/cropsci1962.0011183X000200020033x]
57. Kneebone, W.R. Breeding for seedling vigor. The Biology and Utilization of Grasses; Younger, V.B.; McKell, C.M. Academic Press: New York, NY, USA, 1972; pp. 90-100.
58. Harris, G.A.; Wilson, A.M. Competition for moisture among seedlings of annual and perennial grasses as influenced by root elongation at low temperature. Ecology; 1970; 51, pp. 530-534. [DOI: https://dx.doi.org/10.2307/1935392]
59. Larson, J.E.; Sheley, R.L.; Hardegree, S.P.; Doescher, P.S.; James, J.J. Seed and seedling traits affecting critical life stage transitions and recruitment outcomes in dryland grasses. J. Appl. Ecol.; 2015; 52, pp. 199-209. [DOI: https://dx.doi.org/10.1111/1365-2664.12350]
60. Mukherjee, J.R.; Jones, T.A.; Monaco, T.A.; Adler, P.B. Relationship between seed mass and young-seedling growth and morphology among nine bluebunch wheatgrass populations. Rangel. Ecol. Manag.; 2019; 72, pp. 283-291. [DOI: https://dx.doi.org/10.1016/j.rama.2018.11.006]
61. Hamerlynck, E.P.; Denton, E.M.; Davies, K.W.; Boyd, C.S. Photosynthetic regulation in seed heads and flag leaves of sagebrush-steppe bunchgrasses. Conserv. Physiol.; 2019; 7, coz112. [DOI: https://dx.doi.org/10.1093/conphys/coz112]
62. Waldron, B.L.; Jensen, K.B.; Palazzo, A.J.; Cary, T.J.; Robins, J.A.; Peel, M.D.; Ogle, D.G.; St. John, L. ‘Recovery,’ a new western wheatgrass cultivar with improved seedling establishment on rangelands. J. Plant Registr.; 2011; 5, pp. 367-373. [DOI: https://dx.doi.org/10.3198/jpr2010.09.0527crc]
63. Kulpa, S.M.; Leger, E.A. Strong natural selection during plant restoration favors an unexpected suite of plant traits. Evol. Appl.; 2013; 6, pp. 510-523. [DOI: https://dx.doi.org/10.1111/eva.12038]
64. Rogler, G.A. Seed size and seedling vigor in crested wheatgrass. Agron. J.; 1954; 46, pp. 216-220. [DOI: https://dx.doi.org/10.2134/agronj1954.00021962004600050008x]
65. Cahill, J.F., Jr.; Casper, B.B. Investigating the relationship between neighbor root biomass and below ground competition: Field evidence for symmetric competition below ground. Oikos; 2000; 90, pp. 311-320. [DOI: https://dx.doi.org/10.1034/j.1600-0706.2000.900211.x]
66. Larson, J.E.; Funk, J.L. Seedling root responses to soil moisture and the identification of a belowground trait spectrum across three growth forms. New Phytol.; 2016; 210, pp. 827-838. [DOI: https://dx.doi.org/10.1111/nph.13829] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26765506]
67. Sainju, U.M.; Allen, B.L.; Lenssen, A.W.; Ghimire, R.P. Root biomass, root/shoot ratio, and soil water content under perennial grasses with different nitrogen rates. Field Crops Res.; 2017; 210, pp. 183-191. [DOI: https://dx.doi.org/10.1016/j.fcr.2017.05.029]
68. Zheng, W.; Monaco, T.; Jones, T.; Peel, M. Graphical partitioning of seedling phenotypic plasticity of seven cool-season grass species subjected to two watering frequencies. J. Arid Environ.; 2019; 170, 103986. [DOI: https://dx.doi.org/10.1016/j.jaridenv.2019.05.014]
69. Rowe, C.L.J.; Leger, E.A. Competitive seedlings and inherited traits: A test of rapid evolution of Elymus multisetus (big squirreltail) in response to cheatgrass invasion. Evol. Appl.; 2011; 4, pp. 485-498. [DOI: https://dx.doi.org/10.1111/j.1752-4571.2010.00162.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25567997]
70. Atwater, D.Z.; James, J.J.; Leger, E.A. Seedling root traits strongly influence field survival and performance of a common bunchgrass. Basic Appl. Ecol.; 2015; 16, pp. 128-140. [DOI: https://dx.doi.org/10.1016/j.baae.2014.12.004]
71. Foxx, A.J.; Kramer, A.T. Variation in number of root tips influences survival in competition with an invasive grass. J. Arid Environ.; 2020; 179, 104189. [DOI: https://dx.doi.org/10.1016/j.jaridenv.2020.104189]
72. Humphrey, L.D.; Pyke, D.A. Demographic and growth responses of guerrilla and phalanx perennial grass in competitive mixtures. J. Ecol.; 1998; 86, pp. 854-865. [DOI: https://dx.doi.org/10.1046/j.1365-2745.1998.8650854.x]
73. Westoby, M. A plant leaf-height-seed plant ecology strategy scheme. Plant Soil; 1998; 199, pp. 213-227. [DOI: https://dx.doi.org/10.1023/A:1004327224729]
74. Ray-Mukherjee, J.; Jones, T.A.; Adler, P.B.; Monaco, T.A. Immature seedling growth of two North American native perennial bunchgrasses and the invasive grass Bromus tectorum. Rangel. Ecol. Manag.; 2011; 64, pp. 358-365. [DOI: https://dx.doi.org/10.2111/REM-D-10-00101.1]
75. Denton, E.M.; Smith, B.S.; Hamerlynck, E.P.; Sheley, R.L. Seedling defoliation and drought stress: Variation in intensity and frequency affect performance and survival. Rangel. Ecol. Manag.; 2018; 71, pp. 25-34. [DOI: https://dx.doi.org/10.1016/j.rama.2017.06.014]
76. Abbott, L.; Pistorale, S.; Andrés, A. Evaluacion de los componentes del rendimiento en semilla mediante coeficientes de sendero en poblaciones de agropiro alargado. Agriscientia; 2009; 26, pp. 55-62.
77. Wang, Q.; Zhang, T.; Cui, J.; Wang, X.; Zhou, H.; Han, J.; Gislum, R. Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions. PLoS ONE; 2011; 6, e18245. [DOI: https://dx.doi.org/10.1371/journal.pone.0018245] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21533153]
78. Chen, Z.; Niu, J.; Cao, X.; Jiang, W.; Cui, J.; Wang, Q.; Zhang, Q. Seed yield can be explained by altered yield components in field-grown western wheatgrass (Pascopyrum smithii Rydb.). Sci. Rep.; 2019; 9, 17976. [DOI: https://dx.doi.org/10.1038/s41598-019-54586-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31784680]
79. Wilson, R.L. Ecotype Variation in Seedling and Mature Plant Characteristics of Basin Wildrye (Elymus cinereus Scribn. & Merr.). Master’s Thesis; Montana State University: Bozeman, MT, USA, 1963.
80. Moles, A.T.; Westoby, M. Seed size and plant strategy across the whole life cycle. Oikos; 2006; 113, pp. 91-105. [DOI: https://dx.doi.org/10.1111/j.0030-1299.2006.14194.x]
81. Hamerlynck, E.P.; Smith, B.S.; Sheley, R.L.; Svejcar, T.J. Compensatory photosynthesis, water-use efficiency and biomass allocation of defoliated exotic and native bunchgrass seedlings. Rangel. Ecol. Manag.; 2016; 69, pp. 206-214. [DOI: https://dx.doi.org/10.1016/j.rama.2015.12.007]
82. Hamerlynck, E.P.; Ziegenhagen, L.L. Seed head photosynthetic light responses in clipped and unclipped sagebrush steppe bunchgrasses. J. Arid Environ.; 2020; 172, 104031. [DOI: https://dx.doi.org/10.1016/j.jaridenv.2019.104013]
83. Huxman, T.E.; Hamerlynck, E.P.; Smith, S.D. Reproductive allocation and seed production in Bromus madritensis ssp. rubens at elevated atmospheric CO2. Funct. Ecol.; 1999; 13, pp. 769-777. [DOI: https://dx.doi.org/10.1046/j.1365-2435.1999.00366.x]
84. Farquhar, G.D.; Ehrlinger, J.R.; Hubick, K.T. Carbon isotope discrimination and photosynthesis. Ann. Rev. Plant Physiol. Plant Mol. Biol.; 1989; 40, pp. 503-547. [DOI: https://dx.doi.org/10.1146/annurev.pp.40.060189.002443]
85. Edhaie, B.; Hall, A.E.; Farquhar, G.D.; Nguyen, H.T.; Waines, J.G. Water-use efficiency and carbon isotope discrimination in wheat. Crop Sci.; 1991; 31, pp. 1282-1288.
86. Stiller, W.N.; Read, J.J.; Constable, G.A.; Reid, P.E. Selection for water use efficiency traits in a cotton breeding program: Cultivar differences. Crop Sci.; 2005; 45, pp. 1107-1113. [DOI: https://dx.doi.org/10.2135/cropsci2004.0545]
87. Mukherjee, J.R.; Jones, T.A.; Adler, P.B.; Monaco, T.A. Contrasting mechanisms of recovery from defoliation in two Intermountain-native bunchgrasses. Rangel. Ecol. Manag.; 2015; 68, pp. 485-493. [DOI: https://dx.doi.org/10.1016/j.rama.2015.07.011]
88. Kitchen, S.G.; Monsen, S.B. Germination rate and emergence success in bluebunch wheatgrass. J. Range Manag.; 1994; 47, pp. 145-150. [DOI: https://dx.doi.org/10.2307/4002823]
89. Caldwell, M.M.; Richards, J.H.; Johnson, D.A.; Nowak, R.S.; Dzurec, R.S. Coping with herbivory: Photosynthetic capacity and resource allocation in two semiarid Agropyron bunchgrasses. Oecologia; 1981; 50, pp. 14-24. [DOI: https://dx.doi.org/10.1007/BF00378790] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28310058]
90. He, H.; Monaco, T.A.; Jones, T.A. Functional trait differences between native bunchgrasses and the invasive grass Bromus tectorum. Front. Agric. Sci. Eng.; 2018; 5, pp. 139-147. [DOI: https://dx.doi.org/10.15302/J-FASE-2017175]
91. Harris, G.A. Some competitive relationships between Agropyron spicatum and Bromus tectorum. Ecol. Monogr.; 1967; 37, pp. 89-111. [DOI: https://dx.doi.org/10.2307/2937337]
92. Hardegree, S.P.; Moffet, C.A.; Roundy, B.A.; Jones, T.A.; Novak, S.J.; Clark, P.E.; Pierson, F.B.; Flerchinger, G.N. A comparison of cumulative-germination response of cheatgrass (Bromus tectorum L.) and five perennial bunchgrass species to simulated field-temperature regimes. Environ. Experiment. Bot.; 2010; 69, pp. 320-327. [DOI: https://dx.doi.org/10.1016/j.envexpbot.2010.04.012]
93. MacKown, C.T.; Jones, T.A.; Johnson, D.A.; Monaco, T.A.; Redinbaugh, M.G. Nitrogen uptake by perennial and invasive annual grass seedlings: Nitrogen form effects. Soil Sci. Soc. Am. J.; 2009; 73, pp. 1864-1870. [DOI: https://dx.doi.org/10.2136/sssaj2008.0334]
94. Walker, J.T.; James, J.J.; Drenovsky, R.E. Competition from Bromus tectorum removes differences between perennial grasses in N capture and conservation strategies. Plant Soil; 2016; 412, pp. 177-188. [DOI: https://dx.doi.org/10.1007/s11104-016-3053-4]
95. Blaisdell, J.P.; Pechanec, J.F. Effects of herbage removal at various dates on vigor of bluebunch wheatgrass and arrowleaf balsamroot. Ecology; 1949; 30, pp. 298-305. [DOI: https://dx.doi.org/10.2307/1932611]
96. Mueggler, W.F. Influence of competition on the response of bluebunch wheatgrass to clipping. J. Range Manag.; 1972; 25, pp. 88-92. [DOI: https://dx.doi.org/10.2307/3896791]
97. Busso, C.A.; Richards, J.H. Drought and clipping effects on tiller demography and growth of two tussock grasses in Utah. J. Arid Environ.; 1995; 29, pp. 239-251. [DOI: https://dx.doi.org/10.1016/S0140-1963(05)80093-X]
98. Mueller, R.J.; Richards, J.H. Morphological analysis of tillering in Agropyron spicatum and Agropyron desertorum. Ann. Bot.; 1986; 58, pp. 911-921. [DOI: https://dx.doi.org/10.1093/oxfordjournals.aob.a087273]
99. Jones, T.A.; Nielson, D.C. Defoliation tolerance of bluebunch and Snake River wheatgrasses. Agron. J.; 1997; 89, pp. 270-275. [DOI: https://dx.doi.org/10.2134/agronj1997.00021962008900020019x]
100. Daer, T.; Willard, E.E. Total nonstructural carbohydrate trends in bluebunch wheatgrass related to growth and phenology. J. Range Manag.; 1981; 34, pp. 377-379. [DOI: https://dx.doi.org/10.2307/3897908]
101. Richards, J.H.; Caldwell, M.M. Soluble carbohydrates, concurrent photosynthesis and efficiency in regrowth following defoliation: A field study with Agropyron species. J. Appl. Ecol.; 1985; 22, pp. 907-920. [DOI: https://dx.doi.org/10.2307/2403239]
102. Nowak, R.S.; Caldwell, M.M. A test of compensatory photosynthesis in the field: Implications for herbivory tolerance. Oecologia; 1984; 61, pp. 311-318. [DOI: https://dx.doi.org/10.1007/BF00379627] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28311055]
103. Mukherjee, J.R.; Jones, T.A.; Monaco, T.A. Biomass and defoliation tolerance of 12 populations of Pseudoroegneria spicata at two densities. Rangel. Ecol. Manag.; 2013; 66, pp. 706-713. [DOI: https://dx.doi.org/10.2111/REM-D-13-00049.1]
104. Ott, J.E.; Kilkenny, F.F.; Summers, D.D.; Thompson, T. Long-term vegetation recovery and invasive annual suppression in native and introduced postfire seeding treatments. Rangel. Ecol. Manag.; 2019; 72, pp. 640-653. [DOI: https://dx.doi.org/10.1016/j.rama.2019.02.001]
105. Hull, A.C., Jr. Species for seeding arid rangeland in southern Idaho. J. Range Manag.; 1974; 27, pp. 216-218. [DOI: https://dx.doi.org/10.2307/3897036]
106. Stonecipher, C.A.; Thacker, E.; Welch, K.D.; Ralphs, M.H.; Monaco, T.A. Long-term persistence of cool-season grasses planted to suppress broom snakeweed, downy brome, and weedy forbs. Rangel. Ecol. Manag.; 2019; 72, pp. 266-274. [DOI: https://dx.doi.org/10.1016/j.rama.2018.10.008]
107. Hulvey, K.B.; Aigner, P.A. Using filter-based community assembly models to improve restoration outcomes. J. Appl. Ecol.; 2014; 51, pp. 997-1005. [DOI: https://dx.doi.org/10.1111/1365-2664.12275]
108. Grman, E.; Bassett, T.; Zirbel, C.R.; Brudvig, L.A. Dispersal and establishment filters influence the assembly of restored prairie plant communities. Restor. Ecol.; 2015; 23, pp. 892-899. [DOI: https://dx.doi.org/10.1111/rec.12271]
109. Westbrook, J.W.; Zhang, Q.; Mandal, M.K.; Jenkins, E.V.; Barth, L.E.; Jenkins, J.W.; Grimwood, J.; Schmutz, J.; Holliday, J.A. Optimizing genomic selection for blight resistance in American chestnut backcross populations: A trade-off with American chestnut ancestry implies resistance is polygenic. Evol. Appl.; 2020; 13, pp. 31-47. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31892942][DOI: https://dx.doi.org/10.1111/eva.12886]
110. Larson, S.R.; Jones, T.A.; Hu, Z.M.; McCracken, C.L.; Palazzo, A. Genetic diversity of bluebunch wheatgrass cultivars and a multiple-origin polycross. Crop Sci.; 2000; 40, pp. 1142-1147. [DOI: https://dx.doi.org/10.2135/cropsci2000.4041142x]
111. Georgi, L.L.; Hebard, F.V.; Nelson, C.D.; Staton, M.E.; Olukolu, B.A.; Abbott, A.G. Adapting chestnut single nucleotide polymorphisms for use in breeding. Acta Hort.; 2014; 1019, pp. 105-112. [DOI: https://dx.doi.org/10.17660/ActaHortic.2014.1019.16]
112. Zhebentyayeva, T.; Chandra, A.; Abbott, A.G.; Staton, M.E.; Olukolu, B.A.; Hebard, F.V.; Georgi, L.L.; Jeffers, S.N.; Sisco, P.H.; James, J.B. et al. Genetic and genomic resources for mapping resistance to Phytophthora cinnamomi in chestnut. Acta Hort.; 2014; 1019, pp. 263-270. [DOI: https://dx.doi.org/10.17660/ActaHortic.2014.1019.40]
113. Santos, C.; Nelson, C.D.; Zhebentyayeva, T.; Machado, H.; Gomes-Laranjo, J.; Costa, R.L. First interspecific genetic linkage map for Castanea sativa x Castanea crenata revealed QTLs for resistance to Phytophthora cinnamomi. PLoS ONE; 2017; 12, e0184381. [DOI: https://dx.doi.org/10.1371/journal.pone.0184381]
114. He, J.; Zhao, X.; Laroche, A.; Lu, Z.-X.; Liu, H.L.; Li, Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front. Plant Sci.; 2014; 5, 484. [DOI: https://dx.doi.org/10.3389/fpls.2014.00484]
115. Crain, J.; Larson, S.; Dorn, K.; Hagedorn, T.; DeHaan, L.; Poland, J. Sequenced-based paternity analysis to improve breeding and identify self-incompatibility loci in intermediate wheatgrass (Thinopyrum intermedium). Theor. Appl. Genet.; 2020; 133, pp. 3217-3233. [DOI: https://dx.doi.org/10.1007/s00122-020-03666-1]
116. Isik, F.; Holland, J.; Maltecca, C. Multi environmental trials. Genetic Data Analysis for Plant and Animal Breeding; Isik, F.; Holland, J.; Maltecca, C. Springer International Publishing: Cham, Germany, 2017; pp. 227-262.
117. Piepho, H.-P.; Möhring, J.; Melchinger, A.E.; Büchse, A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica; 2008; 161, pp. 209-228. [DOI: https://dx.doi.org/10.1007/s10681-007-9449-8]
118. Lorenz, A.J.; Chao, S.; Asoro, F.G.; Heffner, E.L.; Hayashi, T.; Iwata, H.; Smith, K.P.; Sorrells, M.E.; Jannink, J.-L. Genomic selection in plant breeding: Knowledge and prospects. Adv. Agron.; 2011; 110, pp. 77-123.
119. Isik, F.; Holland, J.; Maltecca, C. Breeding values. Genetic Data Analysis for Plant and Animal Breeding; Isik, F.; Holland, J.; Maltecca, C. Springer International Publishing: Cham, Germany, 2017; pp. 107-140.
120. Schaeffer, L.R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet.; 2006; 123, pp. 218-223. [DOI: https://dx.doi.org/10.1111/j.1439-0388.2006.00595.x]
121. Heffner, E.L.; Lorenz, A.J.; Jannink, J.-L.; Sorrells, M.E. Plant breeding with genomic selection: Gain per unit time and cost. Crop Sci.; 2010; 50, pp. 1681-1690. [DOI: https://dx.doi.org/10.2135/cropsci2009.11.0662]
122. Isabel, N.; Holliday, J.A.; Aitken, S.N. Forest genomics: Advancing climate adaptation, forest health, productivity, and conservation. Evol. Appl.; 2020; 13, pp. 3-10. [DOI: https://dx.doi.org/10.1111/eva.12902] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31892941]
123. Zhebentyayeva, T.N.; Sisco, P.H.; Georgi, L.L.; Jeffers, S.N.; Perkins, M.T.; James, J.B.; Hebard, F.B.; Saski, C.; Nelson, C.D.; Abbott, A.G. Dissecting resistance to Phytophthora cinnamomi in interspecific hybrid chestnut crosses using sequence-based genotyping and QTL mapping. Phytopathology; 2019; 109, pp. 1594-1604. [DOI: https://dx.doi.org/10.1094/PHYTO-11-18-0425-R] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31287366]
124. Staton, M.; Zhebentyayeva, T.; Olukolu, B.; Fang, G.C.; Nelson, D.; Carlson, J.E.; Abbott, A.G. Substantial genome synteny preservation among woody angiosperm species: Comparative genomics of Chinese chestnut (Castanea mollissima) and plant reference genomes. BMC Genom.; 2015; 16, 744. [DOI: https://dx.doi.org/10.1186/s12864-015-1942-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26438416]
125. Leger, E.A.; Agneray, A.C.; Baughman, O.W.; Brummer, E.C.; Erickson, T.E.; Hufford, K.M.; Kettenring, K.M. Integrating evolutionary potential and ecological function into agricultural seed production to meet demands for the decade of restoration. Restor. Ecol.; 2021; e13543. [DOI: https://dx.doi.org/10.1111/rec.13543]
126. Mastrangelo, S.; Portolano, B.; Di Gerlando, R.; Ciampolini, R.; Tolone, M.; Sardina, M.T. Genome-wide analysis in endangered populations: A case study in Barbaresca sheep. Animal; 2017; 11, pp. 1107-1116. [DOI: https://dx.doi.org/10.1017/S1751731116002780]
127. Hohenlohe, P.A.; Funk, W.C.; Rajora, O.P. Population genomics for wildlife conservation and management. Mol. Ecol.; 2021; 30, pp. 62-82. [DOI: https://dx.doi.org/10.1111/mec.15720]
128. Kosch, T.A.; Waddle, A.W.; Cooper, C.A.; Zenger, K.R.; Garrick, D.J.; Berger, L.; Skerratt, L.F. Genetic approaches for increasing fitness in endangered species. Trends Ecol. Evol.; 2022; 37, pp. 332-345. [DOI: https://dx.doi.org/10.1016/j.tree.2021.12.003]
129. Daetwyler, H.D.; Villanueva, B.; Bijma, P.; Woolliams, J.A. Inbreeding in genome-wide selection. J. Anim. Breed. Genet.; 2007; 124, pp. 369-376. [DOI: https://dx.doi.org/10.1111/j.1439-0388.2007.00693.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18076474]
130. Sonesson, A.K.; Woolliams, J.A.; Meuwissen, T.H.E. Genomic selection requires genomic control of inbreeding. Genet. Select. Evol.; 2012; 44, 27. [DOI: https://dx.doi.org/10.1186/1297-9686-44-27] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22898324]
131. Goddard, M. Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica; 2009; 136, pp. 245-257. [DOI: https://dx.doi.org/10.1007/s10709-008-9308-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18704696]
132. Lin, Z.; Cogan, N.O.I.; Pembleton, L.W.; Spangenberg, G.C.; Forster, J.W.; Hayes, B.J.; Daetwyler, H.D. Genetic gain and inbreeding from genomic selection in a simulated commercial breeding program for perennial ryegrass. Plant Genome; 2016; 9, pp. 1-12. [DOI: https://dx.doi.org/10.3835/plantgenome2015.06.0046]
133. Lin, Z.; Shi, F.; Hayes, B.J.; Daetwyler, H.D. Mitigation of inbreeding while preserving genetic gain in genomic breeding programs for outbred plants. Theor. Appl. Genet.; 2017; 130, pp. 969-980. [DOI: https://dx.doi.org/10.1007/s00122-017-2863-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28364262]
134. Lozada-Soto, E.A.; Maltecca, C.; Lu, D.; Miller, S.; Cole, J.B.; Tiezzi, F. Trends in genetic diversity and the effect of inbreeding in American Angus cattle under genomic selection. Genet. Select. Evol.; 2021; 53, 50. [DOI: https://dx.doi.org/10.1186/s12711-021-00644-z]
135. Wolc, A.; Zhao, H.H.; Arango, J.; Settar, P.; Fulton, J.E.; O’Sullivan, N.P.; Preisinger, R.; Stricker, C.; Habier, D.; Fernando, R.L. et al. Response and inbreeding from a genomic selection experiment in layer chickens. Genet. Select. Evol.; 2015; 47, 59. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26149977][DOI: https://dx.doi.org/10.1186/s12711-015-0133-5]
136. De Beukelaer, H.; Badke, Y.; Fack, V.; De Meyer, G. Moving beyond managing realized genomic relationship in long-term genomic selection. Genetics; 2017; 206, pp. 1127-1138. [DOI: https://dx.doi.org/10.1534/genetics.116.194449]
137. Gorjanc, G.; Hickey, J.M. AlphaMate: A program for optimizing selection, maintenance of diversity and mate allocation in breeding programs. Bioinformatics; 2018; 34, pp. 3408-3411. [DOI: https://dx.doi.org/10.1093/bioinformatics/bty375]
138. Kilkenny, F.F. Genecological approaches to predicting the effects of climate change on plant populations. Nat. Areas J.; 2015; 35, pp. 152-164. [DOI: https://dx.doi.org/10.3375/043.035.0110]
139. Williams, J.W.; Jackson, S.T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ.; 2007; 5, pp. 475-482. [DOI: https://dx.doi.org/10.1890/070037]
140. Starzomski, B.M. Novel ecosystems and climate change. Novel Ecosystems—Intervening in the New Ecological World Order; Hobbs, R.J.; Higgs, E.S.; Hall, C.M. Wiley-Blackwell: Chichester, UK, 2013; pp. 88-101.
141. Stanturf, J.A.; Palik, B.J.; Williams, M.I.; Dumroese, R.K.; Madsen, P. Forest restoration paradigms. J. Sustain. For.; 2014; 33, pp. S161-S194. [DOI: https://dx.doi.org/10.1080/10549811.2014.884004]
142. Zeldin, J.; Lichtenberger, T.M.; Foxx, A.J.; Williams, E.W.; Kramer, A.T. Intraspecific functional trait structure of restoration-relevant species: Implications for restoration seed sourcing. J. Appl. Ecol.; 2020; 57, pp. 864-874. [DOI: https://dx.doi.org/10.1111/1365-2664.13603]
143. Schwartz, M.W.; Brigham, C.A.; Hoeksema, J.D.; Lyons, K.G.; Mills, M.H.; van Mantgem, P.J. Linking biodiversity to ecosystem function: Implications for conservation ecology. Oecologia; 2000; 122, pp. 297-305. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28308280][DOI: https://dx.doi.org/10.1007/s004420050035]
144. Luck, G.W.; Daily, G.C.; Ehrlich, P.R. Population diversity and ecosystem services. Trends Ecol. Evol.; 2003; 18, pp. 331-336. [DOI: https://dx.doi.org/10.1016/S0169-5347(03)00100-9]
145. Meinke, C.W.; Knick, S.T.; Pyke, D.A. A spatial model to prioritize sagebrush landscapes in the Intermountain West (USA) for restoration. Restor. Ecol.; 2009; 17, pp. 652-659. [DOI: https://dx.doi.org/10.1111/j.1526-100X.2008.00400.x]
146. Bradley, B.A.; Houghton, R.A.; Mustard, J.F.; Hamburg, S.P. Invasive grass reduces aboveground carbon stocks in shrublands of the western US. Glob. Chang. Biol.; 2006; 12, pp. 1815-1822. [DOI: https://dx.doi.org/10.1111/j.1365-2486.2006.01232.x]
147. James, J.J.; Davies, K.W.; Sheley, R.L.; Aanderud, Z.T. Linking nitrogen partitioning and species abundance to invasion resistance in the Great Basin. Oecologia; 2008; 156, pp. 636-648. [DOI: https://dx.doi.org/10.1007/s00442-008-1015-0]
148. Davies, K.W. Plant community diversity and native plant abundance decline with increasing abundance of an exotic grass. Oecologia; 2011; 167, pp. 481-491. [DOI: https://dx.doi.org/10.1007/s00442-011-1992-2]
149. Perkins, L.B.; Nowak, R.S. Soil conditioning and plant-soil feedbacks affect competitive relationships between native and invasive grasses. Plant Ecol.; 2012; 213, pp. 1337-1344. [DOI: https://dx.doi.org/10.1007/s11258-012-0092-7]
150. Chambers, J.C.; Miller, R.F.; Board, D.I.; Pyke, D.A.; Roundy, B.A.; Grace, J.B.; Schupp, E.W.; Tausch, R.J. Resilience and resistance of sagebrush ecosystems: Implications for state and transition models and management treatments. Rangel. Ecol. Manag.; 2014; 67, pp. 440-454. [DOI: https://dx.doi.org/10.2111/REM-D-13-00074.1]
151. Davies, K.W.; Boyd, C.S.; Beck, J.L.; Bates, J.D.; Svejcar, T.J.; Gregg, M.A. Saving the sagebrush sea: An ecosystem conservation plan for big sagebrush plant communities. Biol. Conserv.; 2011; 144, pp. 2573-2584. [DOI: https://dx.doi.org/10.1016/j.biocon.2011.07.016]
152. Jones, T.A. Native seeds in the marketplace: Meeting restoration needs in the Intermountain West, United States. Rangel. Ecol. Manag.; 2019; 72, pp. 1017-1029. [DOI: https://dx.doi.org/10.1016/j.rama.2019.07.009]
153. Pickford, G.D. The influence of continued heavy grazing and of promiscuous burning on spring-fall ranges in Utah. Ecology; 1932; 13, pp. 159-171. [DOI: https://dx.doi.org/10.2307/1931066]
154. Mack, R.N.; Thompson, J.N. Evolution in steppe with few large, hooved animals. Am. Nat.; 1982; 119, pp. 757-773. [DOI: https://dx.doi.org/10.1086/283953]
155. Morris, L.R.; Rowe, R.J. Historical land use and altered habitats in the Great Basin. J. Mammal.; 2014; 95, pp. 1144-1156. [DOI: https://dx.doi.org/10.1644/13-MAMM-S-169]
156. Evans, R.A.; Young, J.A. Microsite requirements for downy brome (Bromus tectorum) infestation and control on sagebrush rangelands. Weed Sci.; 1984; 32, (Suppl. 1), pp. 13-17. [DOI: https://dx.doi.org/10.1017/S0043174500060197]
157. D’Antonio, C.M.; Vitousek, P.M. Biological invasions by exotic grasses, the grass/fire cycle, and global change. Ann. Rev. Ecol. Syst.; 1992; 23, pp. 63-87. [DOI: https://dx.doi.org/10.1146/annurev.es.23.110192.000431]
158. Chambers, J.C.; Bradley, B.A.; Brown, C.S.; D’Antonio, C.; Germino, M.J.; Grace, J.B.; Hardegree, S.P.; Miller, R.F.; Pyke, D.A. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in cold desert shrublands of western North America. Ecosystems; 2014; 17, pp. 360-375. [DOI: https://dx.doi.org/10.1007/s10021-013-9725-5]
159. Briske, D.D.; Bestelmeyer, B.T.; Stringham, T.K.; Shaver, P.L. Recommendations for development of resilience-based state-and-transition models. Rangel. Ecol. Manag.; 2008; 61, pp. 359-367. [DOI: https://dx.doi.org/10.2111/07-051.1]
160. Bagchi, S.; Singh, N.J.; Briske, D.D.; Bestelmeyer, B.T.; McClaran, M.P.; Murthy, K. Quantifying long-term plant community dynamics with movement models: Implications for ecological resilience. Ecol. Appl.; 2017; 27, pp. 1514-1528. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28370777][DOI: https://dx.doi.org/10.1002/eap.1544]
161. Morris, L.R.; Monaco, T.A.; Sheley, R.L. Land-use legacies and vegetation recovery 90 years after cultivation in Great Basin sagebrush ecosystems. Rangel. Ecol. Manag.; 2011; 64, pp. 488-497. [DOI: https://dx.doi.org/10.2111/REM-D-10-00147.1]
162. Hardegree, S.P.; Jones, T.A.; Roundy, B.A.; Shaw, N.L.; Monaco, T.A. Assessment of range planting as a conservation practice. Rangel. Ecol. Manag.; 2016; 69, pp. 237-247. [DOI: https://dx.doi.org/10.1016/j.rama.2016.04.007]
163. Clements, C.C.; Harmon, D.N.; Blank, R.R.; Weltz, M. Improving seeding success on cheatgrass-infested rangelands in northern Nevada. Rangelands; 2017; 39, pp. 174-181. [DOI: https://dx.doi.org/10.1016/j.rala.2017.10.003]
164. Broadhurst, L.M.; Lowe, A.; Coates, D.J.; Cunningham, S.A.; McDonald, M.; Vesk, P.A.; Yates, C. Seed supply for broadscale restoration: Maximizing evolutionary potential. Evol. Appl.; 2008; 1, pp. 587-597. [DOI: https://dx.doi.org/10.1111/j.1752-4571.2008.00045.x]
165. Godefroid, S.; Piazza, C.; Rossi, G.; Buord, S.; Stevens, A.-D.; Aguraiuja, R.; Cowell, C.; Weekley, C.W.; Vogg, G.; Iriondo, J.N. et al. How successful are plant species reintroductions?. Biol. Conserv.; 2011; 144, pp. 672-682. [DOI: https://dx.doi.org/10.1016/j.biocon.2010.10.003]
166. Svejcar, T.; Boyd, C.S.; Davies, K.; Hamerlynck, E.; Svejcar, L. Challenges and limitations to native species restoration in the Great Basin, USA. Plant Ecol.; 2017; 218, pp. 81-94. [DOI: https://dx.doi.org/10.1007/s11258-016-0648-z]
167. Germino, M.J.; Fisk, M.R.; Applestein, C. Bunchgrass root abundances and their relationship to resistance and resilience of burned shrub-steppe landscape. Rangel. Ecol. Manag.; 2019; 72, pp. 783-790. [DOI: https://dx.doi.org/10.1016/j.rama.2019.04.001]
168. Reisner, M.D.; Grace, J.B.; Pyke, D.A.; Doescher, P.S. Conditions favouring Bromus tectorum dominance of endangered sagebrush steppe ecosystems. J. Appl. Ecol.; 2013; 50, pp. 1039-1049. [DOI: https://dx.doi.org/10.1111/1365-2664.12097]
169. Austin, D.D.; Stevens, R.; Jorgensen, K.R.; Urness, P.J. Preferences of mule deer for 16 grasses found on Intermountain winter ranges. J. Range Manag.; 1994; 47, pp. 306-311. [DOI: https://dx.doi.org/10.2307/4002552]
170. Riginos, C.; Monaco, T.A.; Veblen, K.E.; Gunnell, K.; Thacker, E.; Dahlgren, D.; Messmer, T. Potential for post-fire recovery of Greater Sage-grouse habitat. Ecosphere; 2019; 10, e02870. [DOI: https://dx.doi.org/10.1002/ecs2.2870]
171. Davies, K.W.; Bates, J.D.; Svejcar, T.J.; Boyd, C.S. Effects of long-term livestock grazing on fuel characteristics in rangelands: An example from the sagebrush steppe. Rangel. Ecol. Manag.; 2010; 63, pp. 662-669. [DOI: https://dx.doi.org/10.2111/REM-D-10-00006.1]
172. Svejcar, T.; Boyd, C.; Davies, K.; Madsen, M.; Bates, J.; Sheley, R.; Marlow, C.; Bohnert, D.; Borman, M.; Mata-Gonzalez, R. et al. Western land managers will need all available tools for adapting to climate change, including grazing: A critique of Beschta et al. Env. Manag.; 2014; 53, pp. 1035-1038. [DOI: https://dx.doi.org/10.1007/s00267-013-0218-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24399203]
173. Paterson, A.H.; Lander, E.S.; Hewitt, J.D.; Peterson, S.; Lincoln, S.E.; Tanksley, S.D. Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature; 1988; 335, pp. 721-726. [DOI: https://dx.doi.org/10.1038/335721a0]
174. Bernardo, R. Molecular markers and selection for complex traits in plants: Learning from the last 20 years. Crop Sci.; 2008; 48, pp. 1649-1664. [DOI: https://dx.doi.org/10.2135/cropsci2008.03.0131]
175. Larson, S.R.; Kellogg, E.A. Genetic dissection of seed production traits and identification of a major-effect seed retention QTL in hybrid Leymus (Triticeae) wildryes. Crop Sci.; 2009; 49, pp. 29-40. [DOI: https://dx.doi.org/10.2135/cropsci2008.05.0277]
176. Collard, B.C.Y.; Mackill, D.J. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philos. Trans. R. Soc. B Biol. Sci.; 2008; 363, pp. 557-572. [DOI: https://dx.doi.org/10.1098/rstb.2007.2170]
177. Jahoor, A.; Eriksen, L.; Backes, G. QTLs and genes for disease resistance in barley and wheat. Cereal Genomics; Gupta, P.K.; Varshney, R.K. Springer: Dordrecht, The Netherlands, 2005; pp. 199-215.
178. Ullah, K.N.; Li, N.; Shen, T.; Wang, P.; Tang, W.; Ma, S.; Zhang, Z.; Jia, H.; Kong, Z.; Ma, Z. Fine mapping of powdery mildew resistance gene Pm4e in bread wheat (Triticum aestivum L.). Planta; 2018; 248, pp. 1319-1328. [DOI: https://dx.doi.org/10.1007/s00425-018-2990-y]
179. Abhijith, K.P.; Muthusamy, V.; Chhabra, R.; Dosad, S.; Bhatt, V.; Chand, G.; Jaiswal, S.K.; Zunjare, R.U.; Vasudev, S.; Yadava, D.K. et al. Development and validation of breeder-friendly gene-based markers for lpa1-1 and lpa2-1 genes conferring low phytic acid in maize kernel. 3 Biotech; 2020; 10, 121. [DOI: https://dx.doi.org/10.1007/s13205-020-2113-x]
180. Lander, E.S.; Botstein, D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics; 1989; 121, pp. 185-199. [DOI: https://dx.doi.org/10.1093/genetics/121.1.185]
181. Meuwissen, T.H.E. Accuracy of breeding values of ‘unrelated’ individuals predicted by dense SNP genotyping. Genet. Select. Evol.; 2009; 41, 35. [DOI: https://dx.doi.org/10.1186/1297-9686-41-35] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19519896]
182. Notice to Release Anatone Germplasm Bluebunch Wheatgrass (Selected Class Natural Population). Available online: https://www.fs.usda.gov/treesearch/pubs/40777 (accessed on 18 July 2022).
183. Jones, T.A.; Mott, I.W. Notice of release of Columbia Germplasm of bluebunch wheatgrass. Nativ. Plants J.; 2016; 17, pp. 53-58. [DOI: https://dx.doi.org/10.3368/npj.17.1.53]
184. Jones, T.A.; Larson, S.R.; Nielson, D.C.; Young, S.A.; Chatterton, N.J.; Palazzo, A.J. Registration of P-7 bluebunch wheatgrass germplasm. Crop Sci.; 2002; 42, pp. 1754-1755. [DOI: https://dx.doi.org/10.2135/cropsci2002.1754]
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Abstract
Effective native plant materials are critical to restoring the structure and function of extensively modified ecosystems, such as the sagebrush steppe of North America’s Intermountain West. The reestablishment of native bunchgrasses, e.g., bluebunch wheatgrass (Pseudoroegneria spicata [Pursh] À. Löve), is the first step for recovery from invasive species and frequent wildfire and towards greater ecosystem resiliency. Effective native plant material exhibits functional traits that confer ecological fitness, phenotypic plasticity that enables adaptation to the local environment, and genetic variation that facilitates rapid evolution to local conditions, i.e., local adaptation. Here we illustrate a multi-disciplinary approach based on genomic selection to develop plant materials that address environmental issues that constrain local populations in altered ecosystems. Based on DNA sequence, genomic selection allows rapid screening of large numbers of seedlings, even for traits expressed only in more mature plants. Plants are genotyped and phenotyped in a training population to develop a genome model for the desired phenotype. Populations with modified phenotypes can be used to identify plant syndromes and test basic hypotheses regarding relationships of traits to adaptation and to one another. The effectiveness of genomic selection in crop and livestock breeding suggests this approach has tremendous potential for improving restoration outcomes for species such as bluebunch wheatgrass.
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1 USDA-Agricultural Research Service, Forage & Range Research Laboratory, 696 North 1100 East, Logan, UT 84322, USA;
2 USDA-Agricultural Research Service, Range & Meadow Forage Management Research Laboratory, 67826-A Highway 205, Burns, OR 97720, USA;
3 Department of Plant Pathology, Kansas State University, 1712 Claflin Road, 4024 Throckmorton PSC, Manhattan, KS 66506, USA;