Andrés J. Cortés
Manuela Restrepo-Montoya
Larry E. Bedoya-Canas- 1Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, Rionegro, Colombia
- 2Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia – Sede Medellín, Medellín, Colombia
Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of “selective sweeps.” Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees’ phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging “big data” approaches (machine learning—ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.
Introduction
How trees will respond to climate change is a pressing question both in the contexts of natural forests and tree plantations (Kremer et al., 2014; Holliday et al., 2017; Isabel et al., 2020). Forests offer key ecological services, boosting significant resources of biodiversity in terms of species and habitats, while help mitigating the impact of excess air pollutants (Phillips et al., 2019; Pennisi, 2020). Trees also source natural renewable materials (i.e., wood itself, cellulose for the pulp industry, and lignin and hemicelluloses for energy production), likely to increase in the future as sustainable alternatives to fossil fuels (Carlson et al., 2014).
Yet, forest tree species are being threatened by climate change (Sullivan et al., 2020) due to fluctuations in the frequency and intensity of heat, drought, salinity (Naidoo et al., 2019), and the incidence of pathogens and pests (Naidoo et al., 2014; Christie et al., 2015). Hence, now more than ever it is essential to explore changing abiotic (Chakhchar et al., 2017; Alcaide et al., 2019b) and biotic (Meyer et al., 2016) interactions. Rampant phenotypic plasticity (Berlin et al., 2017; Hallingback et al., 2019) to climate gradients is presumed in trees, arguing resilience to variability throughout their long lives. Still, forests adaptability should also be assessed in the light of spatially varying local environmental selective pressures (Savolainen et al., 2013), and trees’ genetic and evolutionary potentials (Howe and Brunner, 2005). Both directly reflect and feedback overall adaptive genetic variation. Hence, understanding the genomic drivers that underpin adaptive trait variation becomes vital for conservation and industrial goals.
Developments in plant genomics (Brunner et al., 2007a; Neale and Kremer, 2011) have already disclosed the genetic basis of various useful traits (Khan and Korban, 2012; Tuskan et al., 2018). Yet, this information has limitedly been utilized in tree improvement and conservation (Flanagan et al., 2018), despite genetic gains (Figure 1) and optimized management are urgently required due to environmental issues (Scherer et al., 2020). Besides, breeding woody perennials is primarily bottlenecked by their outcrossing reproductive systems, prolonged juvenile phases (Grattapaglia et al., 2018), large genome sizes lacking elimination mechanisms of long-terminal transposons (Nystedt et al., 2013), and an excessive focus on productivity (Burdon and KlápšTě, 2019) that omits adaptive traits (Table 1; Li et al., 2019). Thus, here we discuss ways to side step these limitations by arguing how predictive genomics can increase selection accuracy and shorten generation intervals (Grattapaglia et al., 2018), assist the detection of exotic variants from tree germplasm (Migicovsky and Myles, 2017), and disclose the genomic potential of adaptation to different climates (Lind et al., 2018). These efforts will ultimately inform conservation and breeding to enhance forest health, ecosystem services, and sustainable production.
Figure 1. Trans-disciplinary approaches (arrows) such as predictive breeding (GP) and machine learning (ML) promise supporting genome-wide marker-assisted (MAS) pre-breeding and breeding strategies for the selection of (A) “plus trees” in the wild, key (B) intra- and (C) inter- specific parental combinations, and (D) elite offspring from those parents. GP and ML should go beyond breeding and feedback (E) germplasm utilization and environmental niche classification (Cortés et al., 2013) and enviromics (Costa-Neto et al., 2020; Resende et al., 2020). Genomic-assisted characterizations, such as Genome-Wide Association Studies—GWAS (Neale and Savolainen, 2004), Genome–Environment Associations—GEA (Rellstab et al., 2015; Cortés and Blair, 2018; López-Hernández and Cortés, 2019) and Genome-Wide Selection Scans—GWSS (Zahn and Purnell, 2016), must also start considering more thoroughly (F) novel sources of local adaptation, (G) genetic-guided infusions and assisted gene flow (AGF), as well an overall systems genetics thinking (Ingvarsson et al., 2016; Myburg et al., 2019).
Table 1. Predictive breeding (genomic prediction—GP, also known as genomic selection—GS) studies in forest tree species published during the last years.
Predictive Breeding Promises Boosting Forest Tree Genetic Improvement
The aim of forest tree breeding is rarely to develop new varieties, but instead advance gradual population improvement through recurrent selection and testing (Neale and Kremer, 2011). Because of the long generation times of forest trees, their breeding has traditionally relied on phenotypic selection from natural stands by choosing “plus-trees” (Figure 1A). Their superior phenotype (primarily productivity and tree architecture, and seldom adaptability) is often measured in situ or in provenance trials. This starting pool of preferred trees constitutes the base population, an arboretum from which further selection is carried out to build a selected population with elite seed/scion donors. Their estimated combinatory ability is gathered from genetic tests such as progeny trials, and parental re-selection (Figure 1B) from top families and single trees (White et al., 2007). After three steps of selection (from the natural, base, and selected populations), eroded genetic diversity may jeopardize overall population’s productivity and resilience due to inbreeding depression. In order to minimize this risk, a breeding population is established to increase genetic variability. Intermating may rely on infusions from external populations. Outbred multi-parental populations (Scott et al., 2020) hence become the base population of a second generation. A bottleneck of this approach is that each generation would last at least nine or 18 years, for seedling or elite clone identification, respectively, in a fast growing tree species such as Eucalyptus (Resende et al., 2012).
Shortcuts to speed up the traditional cycle of forest tree genetic improvement rely on hybrids and backcrossing. Hybrid breeding (Figure 1C) aims harnessing heterotic effects (hybrid vigor) due to dominance and over-dominance already existing in nature, capable of increasing yield and adaptability (Schilthuizen et al., 2004; Seehausen, 2004). Dominance refers to the masking of deleterious effects of recessive alleles as a consequence of the increased heterozygosity resulting from hybridization (i.e., an scape from inbreeding depression). On the other hand, over-dominance corresponds to the increase in aptitude as the result of the additive and epistatic effects of alleles that are naturally maintained by balancing selection and only coincide in hybrid genotypes. Hybrid breeding is nowadays widely used at operational plantations to maximize circumference at breast height (e.g., E. grandis× E. nitens and Pinus elliotti × P. oocarpa), height (e.g., P. caribaea × P. tecunumanii) and resistance to Fusarium spp. (i.e., P. patula × P. tecunumanii), among other potential uses (Burkhart et al., 2017). Backcrossing helps targeting the introgression of desired traits from exotic sources into elite populations, as has been done to transfer resistance to chestnut blight into American populations from Chinese wild donors (Cipollini et al., 2017).
Molecular breeding approaches (Badenes et al., 2016), in which genetic markers are used to assist selection, offer promising alternatives to speed up traditional tree breeding cycles, as well as hybrid and backcrossing schemes. Marker-Assisted Selection—MAS (Butcher and Southerton, 2007; Muranty et al., 2014) and Backcrossing—MAB (Herzog and Frisch, 2011) provide frameworks to pyramid target genetic variants of simple Mendelian traits, which are those regulated by few major genes (e.g., resistance to biotic stresses). Gene editing (Doudna and Charpentier, 2014; Dort et al., 2020) and transgenics (Campbell et al., 2003) can also transfer or silence allelic variants of major effects within a single generation (Pereira-Lorenzo et al., 2019). These may replicate the success of tolerant chestnuts (Alcaide et al., 2019a; Westbrook et al., 2019) and promote reproductive sterility (Meilan et al., 2001; Fritsche et al., 2018). Yet, molecular breeding via MAS, MAB and gene editing is often inefficient to trace quantitative traits as growth and adaptation to abiotic stresses. Adaptation is often polygenic (Cortés et al., 2018b; Barghi et al., 2020) due to many low-effect genes and their second-order interactions (Boyle et al., 2017).
A last-generation predictive breeding (Figure 1D) approach designed for quantitative polygenic traits is known as Genomic Prediction—GP (Desta and Ortiz, 2014; Crossa et al., 2017; Grattapaglia et al., 2018). GP standardizes infinitesimal marker-based additive predictive models by relying on historical phenotypic data (Meuwissen et al., 2001; Gianola et al., 2006; de los Campos et al., 2013). Trait data must be in Linkage Disequilibrium—LD or genetic auto-correlation (e.g., Kelleher et al., 2012), with the molecular markers or with the samples’ genetic co-ancestry. GP utility has been demonstrated (Table 1) in model forest tree species such as Eucalyptus (Resende et al., 2012; Suontama et al., 2019), and conifers as Pinus (Resende M. F. et al., 2012; Li et al., 2019) and Douglas-fir (Thistlethwaite et al., 2017, 2019b), but also in non-model perennial crops such as coffee (Sousa et al., 2018), rubber (Cros et al., 2019; Souza et al., 2019) and oil palm (Cros et al., 2015). GP may even fit epigenetics (Roudbar et al., 2020), as well as multi-trait genomic models as was recently confirmed in Norway spruce for growth, wood quality and weevil resistance traits (Lenz et al., 2020). GP could also be coupled with somatic embryo-genesis for clonal propagation of elite genotypes by selecting elite zygotic embryos based on their genomic breeding value (Grattapaglia et al., 2018). GP has the potential to predict untested hybrid genotypes (Technow et al., 2014) in woody perennials (Cros et al., 2017; Tan et al., 2017) by genotyping potential parental lines and phenotyping few F1 hybrids. Prioritizing inter-specific combinations for field trials can speed up hybrid breeding. Meanwhile, like already envision for chestnut (Westbrook et al., 2020), Genomic-Assisted Backcrossing (GABC) will replace MAB as the strategy to assist introgression breeding into elite populations from exotic germplasm.
Assisting Genomic Characterization of Tree Germplasm to Capture Novel Variants
Exploiting tree wild populations for genomics-assisted breeding (Figure 1E) is key to broaden the genetic basis of woody perennial breeding programs (Migicovsky and Myles, 2017). Specifically, diverse seed bank collections and novel tree provenances might source (Ulian et al., 2020) exotic variation (e.g., unique wood quality properties). They also help avoiding genetic erosion (e.g., via infusions) and increasing long-term adaptability to climate change (e.g., making forests more tolerant to abiotic stresses such as drought and heat). For example, genomic diversity analyses helped capturing rare variants in P. trichocarpa germplasm (Piot et al., 2019) often missed by Genome-Wide Association Studies (GWAS) in the related species P. tremula (Khan and Korban, 2012). Expanded phylogenomic (Wang M. et al., 2020) and species (Wang et al., 2020) diversity may source novel alleles to support selective breeding, as in wood quality traits for improved bioenergy feedstock. In turn, GP might go beyond breeding, the focus of the previous section, and feedback seed bank characterization (Hickey et al., 2017)—e.g., by predicting seed traits (Kehel et al., 2020) and overall yield (Crossa et al., 2007, 2016) in diverse accessions that otherwise could not have been tested at once in genetic field trials. Although the use of GP for germplasm characterization is latent, it has not been fully explored in forest tree species, a main research gap to be filled in the oncoming years.
Tree species rich in evolutionary diversity (Shang et al., 2020) could leverage breeding. Hybridization (Nieto Feliner et al., 2020), introgression (Burgarella et al., 2019), and polyploidy (Mason and Wendel, 2020) have already pumped morphological novelty by testing more genetic compatibilities than humans ever will. Yet, genomics of adaptive radiations (Seehausen, 2004; Madriñán et al., 2013; Cortés et al., 2018a; Marques et al., 2019) are challenging (Schilthuizen et al., 2004; de la Harpe et al., 2017). Long-living oaks—Quercus (Plomion et al., 2018; Leroy et al., 2020b; Plomion and Martin, 2020) are a classical syngameon (Cannon and Petit, 2020) – a promiscuous network of weakly isolated species that has driven peerless historical (Crowl et al., 2020; Hipp et al., 2020; Leroy et al., 2020c) and current (Leroy et al., 2020a) adaptive introgression (Kremer and Hipp, 2020).
In short, marker-assisted schemes are liable to be implemented at various stages during pre-breeding—e.g., in the selection of “plus trees” from the wild (De Dato et al., 2018), of target parental pairs (Blair et al., 2013), and of superior offspring (Galeano et al., 2012). These approaches also aid conservation (Martín et al., 2012; Mattioni et al., 2017) and germplasm tracing (Cortés et al., 2011; Blair et al., 2012; Chiocchini et al., 2016). Still, genomic-assisted studies of germplasm may risk focusing on productive traits and disregard locally adapted trait variation.
Genomics of Adaptation to Different Environments
Local genetic adaptation (Figure 1F) may prove useful in the reaction of forests to climate change (Savolainen et al., 2013; Lascoux et al., 2016), for instance via gene swamping of pre-adapted alleles (Kremer et al., 2014; de Visser et al., 2018). Nowadays there is a wide portfolio of genomic tools that appeal to environmental variables in order to infer the genetic basis of adaptation to abiotic stresses. Specifically, Genome-Wide Selection Scans—GWSS (Zahn and Purnell, 2016) and Genome–Environment Associations – GEA (Rellstab et al., 2015) aim detecting signatures of selection across environmental gradients by pinpointing sections in the genomes that correlate with habitat heterogeneity (Forester et al., 2016). These approaches have successfully been used to assess variation in bud-break phenology (McKown et al., 2018) and stomata patterning (McKown et al., 2014) as potential responses to climate warming in natural populations of P. trichocarpa. They have also allow comparing the likelihoods of adaptive reactions at continental (Holliday et al., 2011; Evans et al., 2014; Zhou et al., 2014; Stölting et al., 2015) and regional scales (Eckert et al., 2010; Holliday et al., 2016; Pluess et al., 2016; Ingvarsson and Bernhardsson, 2020) across phylogenetically diverse taxa (Yeaman et al., 2016). Currently there are even multi-scale approaches to detect widespread divergent selection in non-model tree species experiencing population decline (Mayol et al., 2020).
Local adaptation to climate change can be further enhanced (Figure 1G) via assisted gene flow—AGF (Aitken and Whitlock, 2013). AGF aims minimizing endogenous negative, while maximizing exogenous positive, selection by trans-locating pre-adapted individuals to facilitate adaptation of planted forests to climate change (Aitken and Bemmels, 2016). Management of local adaptation in a changing climate was recently examined in populations from lodgepole pine (P. contorta) across western Canada (Mahony et al., 2020). Yet, operational uses of genomic data to guide seed transfer or AGF are still lacking. Alternatively, genetic containment may be desired for transgenic trees (Brunner et al., 2007b; Klocko et al., 2016). The utility of these approaches in tropical forests remains to be explored. Tropical trees are more at risk from warming because they are closer to upper thermal limits (Freeman et al., 2020; Sentinella et al., 2020), as in montane (Cortés and Wheeler, 2018; Feeley et al., 2020; Tito et al., 2020) and alpine (Wheeler et al., 2014, 2016; Valencia et al., 2020) habitats. Disclosing the genetic, pan-genomic (Bayer et al., 2020), and epigenetic (Brautigam et al., 2013; Sow et al., 2018; Barrera-Redondo et al., 2020) bases of traits underlying adaptive responses in tree species will assist AGF, industrial milestones, and conservation priorities (Isabel et al., 2020) across meta-populations (Gonzalez et al., 2020), and even micro-habitats (Cortés et al., 2014; Abdelaziz et al., 2020).
Concluding Remarks
A major question in the interface between forests and their environments that genomics have the potential to assist is whether tree adaptation to the fast pace of climate change can happen despite their long generation times (Holliday et al., 2017). Specifically, GP offers a feasible way to predict adaptation from allele frequencies in many genes of low effects underlying polygenic traits (Isabel et al., 2020). This way, the role of adaptive responses can be balanced in relation with range shifts (i.e., migration) and extinction as possible climate change outcomes for tree populations (Aitken et al., 2008; Alberto et al., 2013). This question is equally insightful for domesticated and wild stands of forest trees, and must be coupled with reflections regarding the best propagation and conservation schemes. For instance, the factual consequences on genetic diversity of clonal and seedling forestry (Ingvarsson and Dahlberg, 2018), and of assisted gene flow (Aitken and Whitlock, 2013; Aitken and Bemmels, 2016), must be compiled.
Forest genomics tends focusing on economically important species. Yet, the power of population genomics must be further extended to comprehend neutral and adaptive processes in non-commercial species of ecological value in order to advance not just productivity, but also climate adaptation, forest health and conservation (Isabel et al., 2020). In this sense, GP is starting to permeate novel non-key traits other than growth and wood density, but still of interest for breeding, such as branching, stem straightness and external resin bleeding (Li et al., 2019). GP is also predicting adaptive trait variation for abiotic (Eckert et al., 2010) and biotic (Westbrook et al., 2020) stresses. In parallel to an enrichment of target traits, emerging genomic technologies might unlock woody plant trait diversity beyond the model tree species poplar, eucalyptus, willow, oak, chestnut and pecan (Tuskan et al., 2018).
There is currently a rich mosaic of alternative genetic methods to carry out both explicit (direct) and implied (indirect) selection on economic- (Burdon and KlápšTě, 2019) and ecological-worth (Holliday et al., 2017; Isabel et al., 2020) functions. These different traits can enlighten our understanding of the consequences of genetic divergence on the reaction of tree populations to climate change (Kremer et al., 2014). However, novel methodological developments should target more comprehensively complex trait–environment relationships (Bruelheide et al., 2018). They should also mingle between adaptive (Cortés et al., 2015b; Sedlacek et al., 2016) and range shift (Sedlacek et al., 2014; Wheeler et al., 2015) responses across altitudinal (Lenoir et al., 2008; Steinbauer et al., 2018), latitudinal (Chen et al., 2011) and micro-habitat (Sedlacek et al., 2015; Little et al., 2016) gradients.
Perspectives
Exploring natural adaptation to changing climate and genetic breeding for tolerance to abiotic stress in forest tree species has traditionally been assisted by GWAS, GWSS, GEA (Cortés et al., 2020), and AGF techniques. These approaches have allowed identifying and utilizing naturally available, locally adapted, variants. More recently, major developments in the field of predictive breeding (i.e., GP) promise to speed up selection from natural sources, as well as within the breeding cycle, by shortening the generation intervals and increasing the selection accuracy prior field trials. We have already identified and discussed major improvements in this line, such as multi-trait GP models (Lenz et al., 2020), coupled with integrative selection scores (Burdon and KlápšTě, 2019) on novel non-key (Li et al., 2019) and ecological-worth (Holliday et al., 2017; Isabel et al., 2020) traits. These innovations can capture multi-scale trait–environment relationships (Bruelheide et al., 2018) in non-model tree species (Mayol et al., 2020). Given the complexity and heterogeneity of trans-disciplinary data sources, Machine Learning (ML) offers a timely predictive and synthetizing approach capable of merging the highlights of the GWAS, GWSS, GEA, AGF and GP techniques.
“Supervised” ML typically utilizes “labeled” training datasets in order to cross-validate the “recall” rate of a target classification (e.g., selection). ML powerfully handles high-dimensional inputs of heterogeneous “features” without a joint probability distribution (Schrider and Kern, 2018). This way, algorithmically generated non-parametric models that avoid rejection sampling sidestep the “curse of dimensionality” and offer new ways to reveal complex systems (Myburg et al., 2019). ML has historically been utilized in functional genomics (Libbrecht and Noble, 2015) and ecological niche modeling (Phillips et al., 2017). Yet, it is now transitioning into GWAS-coupled MAS (Cortés et al., 2015a), GP (Crossa et al., 2019; Abdollahiarpanahi et al., 2020), GWSS (Schrider and Kern, 2018), and demographics—as when coupled with Approximate Bayesian Computation (Elleouet and Aitken, 2018; Liu et al., 2019).
We anticipate that ML techniques will brace GP predictions for various traits in multi-environment trials that aim disentangling the additive genetic variance and the genotype × environment components. Novel developments in the field of ML will further allow building more accurate predictions by merging environmental variables, microhabitat diversity, and genome-wide divergence, all within a tree-breeding context to pivot “plus tree” selection, hybrid breeding and GABC schemes, as well as in terms of adaptation to climate change in natural forests. Integrative assessments (Ingvarsson et al., 2016) via ML promise harnessing adaptive trait variation in forest tree species.
Author Contributions
AC conceived this review. MR-M and LB-C collected literature and prepared summary tables. AC wrote the first draft of the review with later edits made by MR-M and LB-C.
Funding
Supported to AC during the early phases of this work was made through the grants 4.1-2016-00418 and BS2017-0036 from Vetenskapsrådet (VR) and from Kungliga Vetenskapsakademien (KVA), respectively. The editorial fund from the Colombian Corporation for Agricultural Research (AGROSAVIA) waged the publication of this review.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
This review homages C. Lexer R.I.P. (Fay and Palma-Silva, 2020; Karrenberg et al., 2020; Schloötterer, 2020) for his visionary contributions to the fields of forest tree genetics and population genomics, and for his exceptional enthusiasm while mentoring and welcoming pupils and colleagues at his affable research group. In particular, AC enormously appreciates his stimulating and supportive role as an inspiring doctoral co-advisor (2011–2015), and gratefully remembers his hospitality in Fribourg (Switzerland) during 2011–2013 through countless discussions, refreshing Fondue mealtimes, hikes and Pétanque contests. C. Lexer is also thanked for making possible exciting joint field trips with S. Humbert and Y. Naciri to Col du Sanetsch, Lac de Moiry and Lac des Autannes (Valais, Switzerland) in August 2011, and with S. Humbert and A. Tribsch to Hohe Tauern and Niedere Tauern (Austria) in July 2013, as well as for encouraging thought-provoking scientific discussions during the Swiss National Science Foundation (SNSF) Sinergia Salix Kickoff Meeting held in April 2011 at Davos (Graubünden, Switzerland), the European Society for Evolutionary Biology Congress held in August 2011 at Tubingen (Germany), the Swiss National Science Foundation (SNSF) Sinergia Salix Closure Meeting held in February 2013 at Fribourg (Switzerland), and the European Molecular Biology Organization (EMBO) workshop on Mechanisms of Plant Speciation (Lafon-Placette et al., 2016) held in June 2015 at Åkersberga (Sweden). Special recognition is additionally granted to S. Arenas, J.P. Jaramillo-Correa, F. López-Hernández and M.J. Torres-Urrego for debates while writing this review. Topic editors and reviewers are acknowledged for conceiving and pushing through a timely Research Topic on “Forests and Their Interactions with the Environment”.
References
Abdelaziz, M., Anderson, J. T., Rochford, M. E., Bemmels, J. B., Jameel, M. I., and Denney, D. A. (2020). Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change. AoB PLANTS 12:plaa005. doi: 10.1093/aobpla/plaa005
Abdollahiarpanahi, R., Gianola, D., and Peñagaricano, F. (2020). Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genet. Sel. Evol. 52:12.
Aitken, S. N., and Bemmels, J. B. (2016). Time to get moving: assisted gene flow of forest trees. Evol. Appl. 9, 271–290. doi: 10.1111/eva.12293
Aitken, S. N., and Whitlock, M. C. (2013). Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388. doi: 10.1146/annurev-ecolsys-110512-135747
Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T., and Curtis-Mclane, S. (2008). Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111. doi: 10.1111/j.1752-4571.2007.00013.x
Alberto, F. J., Aitken, S. N., Alia, R., Gonzalez-Martinez, S. C., Hanninen, H., Kremer, A., et al. (2013). Potential for evolutionary responses to climate change - evidence from tree populations. Glob. Chang. Biol. 19, 1645–1661. doi: 10.1111/gcb.12181
Alcaide, F., Solla, A., Cherubini, M., Mattioni, C., Cuenca, B., Camisón, Á., et al. (2019a). Adaptive evolution of chestnut forests to the impact of ink disease in Spain. J. Syst. Evol. 58, 504–516. doi: 10.1111/jse.12551
Alcaide, F., Solla, A., Mattioni, C., Castellana, S., and Martín, M. A. (2019b). Adaptive Diversity and drought tolerance in Castanea Sativa assessed through genic markers Est-Ssr. Forestry 92, 287–296. doi: 10.1093/forestry/cpz007
Badenes, M. L., Fernandez, I. M. A., Rios, G., and Rubio-Cabetas, M. J. (2016). Application of genomic technologies to the breeding of trees. Front. Genet. 7:198. doi: 10.3389/fgene.2016.00198
Ballesta, P., Bush, D., Silva, F. F., and Mora, F. (2020). Genomic predictions using low-density Snp markers, Pedigree and Gwas information: a case study with the non-model species Eucalyptus Cladocalyx. Plants 9:99. doi: 10.3390/plants9010099
Ballesta, P., Maldonado, C., Perez-Rodriguez, P., and Mora, F. (2019). Snp and Haplotype-based genomic selection of quantitative traits in Eucalyptus Globulus. Plants 8:331. doi: 10.3390/plants8090331
Barghi, N., Hermisson, J., and SchloöTterer, C. (2020). Polygenic adaptation: a unifying framework to understand positive selection. Nat. Rev. Genet. doi: 10.1038/s41576-020-0276-2 [Epub ahead of print].
Barrera-Redondo, J., Pinero, D., and Eguiarte, L. E. (2020). Genomic, transcriptomic and epigenomic tools to study the domestication of plants and animals: a field guide for beginners. Front. Genet. 11:742. doi: 10.3389/fgene.2020.00742
Bayer, P. E., Golicz, A. A., Scheben, A., Batley, J., and Edwards, D. (2020). Plant pan-genomes are the new reference. Nat. Plants 6, 914–920. doi: 10.1038/s41477-020-0733-0
Berlin, S., Hallingbäck, H. R., Beyer, F., Nordh, N. E., Weih, M., and Rönnberg-Wästljung, A. C. (2017). Genetics of phenotypic plasticity and biomass traits in hybrid willows across contrasting environments and years. Ann. Bot. 120, 87–100. doi: 10.1093/aob/mcx029
Blair, M. W., Cortés, A. J., Penmetsa, R. V., Farmer, A., Carrasquilla-Garcia, N., and Cook, D. R. (2013). A high-throughput Snp marker system for parental polymorphism screening, and diversity analysis in common bean (Phaseolus Vulgaris L.). Theor. Appl. Genet. 126, 535–548. doi: 10.1007/s00122-012-1999-z
Blair, M. W., Soler, A., and Cortés, A. J. (2012). Diversification and Population Structure in Common Beans (Phaseolus vulgaris L.). PLoS One 7:e49488. doi: 10.1371/journal.pone.0049488
Bouvet, J.-M., Makouanzi Ekomono, C. G., Brendel, O., Laclau, J.-P., Bouillet, J.-P., and Epron, D. (2020). Selecting for water use efficiency, wood chemical traits and biomass with genomic selection in a Eucalyptus breeding program. For. Ecol. Manage. 465:118092. doi: 10.1016/j.foreco.2020.118092
Boyle, E. A., Li, Y. I., and Pritchard, J. K. (2017). An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186. doi: 10.1016/j.cell.2017.05.038
Brautigam, K., Vining, K. J., Lafon-Placette, C., Fossdal, C. G., Mirouze, M., Marcos, J. G., et al. (2013). Epigenetic regulation of adaptive responses of forest tree species to the environment. Ecol. Evol. 3, 399–415. doi: 10.1002/ece3.461
Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., et al. (2018). Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917.
Brunner, A. M., Difazio, S. P., and Groover, A. T. (2007a). Forest genomics grows up and branches out. New Phytol. 174, 707–710.
Brunner, A. M., Li, J., Difazio, S. P., Shevchenko, O., Montgomery, B. E., Mohamed, R., et al. (2007b). Genetic containment of forest plantations. Tree Genet. Genomes 3, 75–100. doi: 10.1007/s11295-006-0067-8
Burdon, R. D., and KlápšTě, J. (2019). Alternative selection methods and explicit or implied economic-worth functions for different traits in tree breeding. Tree Genet. Genomes 15:79.
Burgarella, C., Barnaud, A., Kane, N. A., Jankowski, F., Scarcelli, N., Billot, C., et al. (2019). Adaptive introgression: an untapped evolutionary mechanism for crop adaptation. Front. Plant Sci. 10:4. doi: 10.3389/fpls.2019.00004
Burkhart, H. E., Brunner, A. M., Stanton, B. J., Shuren, R. A., Amateis, R. L., and Creighton, J. L. (2017). An assessment of potential of hybrid poplar for planting in the Virginia Piedmont. New Forests 48, 479–490. doi: 10.1007/s11056-017-9576-6
Butcher, P., and Southerton, S. (2007). “Marker-Assisted Selection in Forestry Species,” in Marker-Assisted Selection – Current Status and Future Perspectives in Crops, Livestock, Forestry and Fish, eds E. Guimarães, J. Ruane, B. Scherf, A. Sonnino, and J. Dargie (Rome: FAO).
Campbell, M. M., Brunner, A. M., Jones, H. M., and Strauss, S. H. (2003). Forestry’s fertile crescent: the application of biotechnology to forest trees. Plant Biotechnol. J. 1, 141–154. doi: 10.1046/j.1467-7652.2003.00020.x
Cannon, C. H., and Petit, R. J. (2020). The oak syngameon: more than the sum of its parts. New Phytol. 226, 978–983. doi: 10.1111/nph.16091
Cappa, E. P., De Lima, B. M., Da Silva-Junior, O. B., Garcia, C. C., Mansfield, S. D., and Grattapaglia, D. (2019). Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step Gblup. Plant Sci. 284, 9–15. doi: 10.1016/j.plantsci.2019.03.017
Carlson, C. H., Gouker, F. E., Serapiglia, M. J., Tang, H., Krishnakumar, V., Town, C. D., et al. (2014). “Annotation of the Salix purpurea L. genome and gene families important for biomass production,” in Proceedings of the Plant and Animal Genetics Conference XXII, San Diego, CA.
Chakhchar, A., Haworth, M., El Modafar, C., Lauteri, M., Mattioni, C., Wahbi, S., et al. (2017). An assessment of genetic diversity and drought tolerance in Argan tree (Argania Spinosa) populations: potential for the development of improved drought tolerance. Front. Plant Sci. 8:276. doi: 10.3389/fpls.2017.00276
Chen, I. C., Hill, J. K., Ohlemuller, R., Roy, D. B., and Thomas, C. D. (2011). Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026. doi: 10.1126/science.1206432
Chen, Z. Q., Baison, J., Pan, J., Westin, J., Gil, M. R. G., and Wu, H. X. (2019). Increased prediction ability in Norway Spruce Trials marker X environment interaction and non-additive genomic selection model. J. Hered. 110, 830–843. doi: 10.1093/jhered/esz061
Chiocchini, F., Mattioni, C., Pollegioni, P., Lusini, I., Martín, M. A., Cherubini, M., et al. (2016). Mapping the genetic diversity of Castanea Sativa: exploiting spatial analysis for biogeography and conservation studies. J. Geogr. Information Syst. 08, 248–259. doi: 10.4236/jgis.2016.82022
Christie, N., Tobias, P. A., Naidoo, S., and Kulheim, C. (2015). The Eucalyptus Grandis Nbs-Lrr gene family: physical clustering and expression hotspots. Front. Plant Sci. 6:1238. doi: 10.3389/fpls.2015.01238
Cipollini, M., Dingley, N. R., Felch, P., and Maddox, C. (2017). Evaluation of phenotypic traits and blight-resistance in an American chestnut backcross orchard in Georgia. Glob. Ecol. Conserv. 10, 1–8. doi: 10.1016/j.gecco.2017.01.004
Cortés, A. J., and Blair, M. W. (2018). Genotyping by sequencing and genome – environment associations in wild common bean predict widespread divergent adaptation to drought. Front. Plant Sci. 9:128. doi: 10.3389/fpls.2018.00128
Cortés, A. J., Chavarro, M. C., and Blair, M. W. (2011). Snp marker diversity in common bean (Phaseolus vulgaris L.). Theor. Appl. Genet. 123, 827–845. doi: 10.1007/s00122-011-1630-8
Cortés, A. J., Garzón, L. N., Valencia, J. B., and Madriñán, S. (2018a). On the causes of rapid diversification in the Páramos: isolation by ecology and genomic divergence in Espeletia. Front. Plant Sci. 9:1700. doi: 10.3389/fpls.2018.01700
Cortés, A. J., Liu, X., Sedlacek, J., Wheeler, J. A., Lexer, C., and Karrenberg, S. (2015a). Maintenance of Female-Bias in a Polygenic Sex Determination System is Consistent with Genomic Conflict. On the Big Challenges of a Small Shrub: Ecological Genetics of Salix Herbacea L. (Uppsala: Acta Universitatis Upsaliensis).
Cortés, A. J., Monserrate, F., Ramírez-Villegas, J., Madriñán, S., and Blair, M. W. (2013). Drought tolerance in wild plant populations: the case of common beans (Phaseolus vulgaris L.). PLoS One 8:e62898. doi: 10.1371/journal.pone.0062898
Cortés, A. J., Skeen, P., Blair, M. W., and Chacón-Sánchez, M. I. (2018b). Does the genomic landscape of species divergence in phaseolus beans coerce parallel signatures of adaptation and domestication? Front. Plant Sci. 9:1816. doi: 10.3389/fpls.2018.01816
Cortés, A. J., Waeber, S., Lexer, C., Sedlacek, J., Wheeler, J. A., Van Kleunen, M., et al. (2014). Small-scale patterns in snowmelt timing affect gene flow and the distribution of genetic diversity in the alpine dwarf shrub Salix Herbacea. Heredity 113, 233–239. doi: 10.1038/hdy.2014.19
Cortés, A. J., and Wheeler, J. A. (2018). “The environmental heterogeneity of mountains at a fine scale in a changing world,” in Mountains, Climate, and Biodiversity, eds C. Hoorn, A. Perrigo, and A. Antonelli (New York, NY: Wiley).
Cortés, A. J., Wheeler, J. A., Sedlacek, J., Lexer, C., and Karrenberg, S. (2015b). Genome-Wide Patterns of Microhabitat-Driven Divergence in the Alpine Dwarf Shrub Salix Herbacea L. On the Big Challenges of a Small Shrub: Ecological Genetics of Salix Herbacea L. (Uppsala: Acta Universitatis Upsaliensis).
Cortés, A. J., López-Hernández, F., and Osorio-Rodriguez, D. (2020). Predicting thermal adaptation by looking into populations’ genomic past. Front. Genet. 11:564515. doi: 10.3389/fgene.2020.564515
Costa-Neto, G., Fritsche-Neto, R., and Crossa, J. (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity doi: 10.1038/s41437-020-00353-1
Cros, D., Bocs, S., Riou, V., Ortega-Abboud, E., Tisné, S., Argout, X., et al. (2017). Genomic preselection with genotyping-by- sequencing increases performance of commercial oil palm hybrid crosses. BMC Genomics 18:839. doi: 10.1186/s12864-017-4179-3
Cros, D., Denis, M., SaìNchez, L., Cochard, B., Flori, A., Durand-Gasselin, T., et al. (2015). Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis Guineensis Jacq.). Theor. Appl. Genet. 128, 397–410. doi: 10.1007/s00122-014-2439-z
Cros, D., Mbo-Nkoulou, L., Bell, J. M., Oum, J., Masson, A., Soumahoro, M., et al. (2019). Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production. Ind. Crops Prod. 138:111464. doi: 10.1016/j.indcrop.2019.111464
Crossa, J., Burgueno, J., Dreisigacker, S., Vargas, M., Herrera-Foessel, S. A., Lillemo, M., et al. (2007). Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics 177, 1889–1913. doi: 10.1534/genetics.107.078659
Crossa, J., Jarquin, D., Franco, J., Perez-Rodriguez, P., Burgueno, J., Saint-Pierre, C., et al. (2016). Genomic prediction of gene bank wheat landraces. G3 6, 1819–1834. doi: 10.1534/g3.116.029637
Crossa, J., Martini, J. W. R., Gianola, D., Perez-Rodriguez, P., Jarquin, D., Juliana, P., et al. (2019). Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials. Front. Genet. 10:1168. doi: 10.3389/fgene.2019.01168
Crossa, J., Perez-Rodriguez, P., Cuevas, J., Montesinos-Lopez, O., Jarquin, D., De Los Campos, G., et al. (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 22, 961–975.
Crowl, A. A., Manos, P. S., Mcvay, J. D., Lemmon, A. R., Lemmon, E. M., and Hipp, A. L. (2020). Uncovering the genomic signature of ancient introgression between white oak lineages (Quercus). New Phytol. 226, 1158–1170. doi: 10.1111/nph.15842
De Dato, G., Teani, A., Mattioni, C., Marchi, M., Monteverdi, M. C., and Ducci, F. (2018). Delineation of seed collection zones based on environmental and genetic characteristics for Quercus Suber L. in Sardinia, Italy. iForest 11, 651–659. doi: 10.3832/ifor2572-011
de la Harpe, M., Paris, M., Karger, D. N., Rolland, J., Kessler, M., Salamin, N., et al. (2017). Molecular ecology studies of species radiations: current research gaps, opportunities and challenges. Mol. Ecol. 26, 2608–2611. doi: 10.1111/mec.14110
de los Campos, G., Hickey, J. M., Pong-Wong, R., Daetwyler, H. D., and Calus, M. P. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345. doi: 10.1534/genetics.112.143313
Desta, Z. A., and Ortiz, R. (2014). Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 19, 592–601. doi: 10.1016/j.tplants.2014.05.006
de Visser, J. A. G. M., Elena, S. F., Fragata, I. S., and Matuszewski, S. (2018). The utility of fitness landscapes and big data for predicting evolution. Heredity 121, 401–405. doi: 10.1038/s41437-018-0128-4
Dort, E. N., Tanguay, P., and Hamelin, R. C. (2020). Crispr/Cas9 gene editing: an unexplored frontier for forest pathology. Front. Plant Sci. 11:1126. doi: 10.3389/fpls.2020.01126
Doudna, J. A., and Charpentier, E. (2014). Genome editing. the new frontier of genome engineering with Crispr-Cas9. Science 346:1258096.
Eckert, A. J., Van Heerwaarden, J., Wegrzyn, J. L., Nelson, C. D., Ross-Ibarra, J., Gonzalez-Martinez, S. C., et al. (2010). Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus Taeda L., Pinaceae). Genetics 185, 969–982. doi: 10.1534/genetics.110.115543
Elleouet, J. S., and Aitken, S. N. (2018). Exploring approximate bayesian computation for inferring recent demographic history with genomic markers in nonmodel species. Mol. Ecol. Resour. 18, 525–540. doi: 10.1111/1755-0998.12758
Evans, L. M., Slavov, G. T., Rodgers-Melnick, E., Martin, J., Ranjan, P., Muchero, W., et al. (2014). Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations. Nat. Genet. 46, 1089–1096. doi: 10.1038/ng.3075
Fay, M. F., and Palma-Silva, C. (2020). Professor Christian Lexer (23.05.1971-15.12.2019). Bot. J. Linn. Soc. 192, 589–591. doi: 10.1093/botlinnean/boaa006
Feeley, K., Martinez-Villa, J., Perez, T., Silva Duque, A., Triviño Gonzalez, D., and Duque, A. (2020). The thermal tolerances, distributions, and performances of tropical montane tree species. Front. For. Glob. Change 3:25. doi: 10.3389/ffgc.2020.00025
Flanagan, S. P., Forester, B. R., Latch, E. K., Aitken, S. N., and Hoban, S. (2018). Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol. Appl. 11, 1035–1052. doi: 10.1111/eva.12569
Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L., and Lasky, J. R. (2016). Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25, 104–120. doi: 10.1111/mec.13476
Freeman, B. G., Song, Y., Feeley, K. J., and Zhu, K. (2020). Montane species and communities track recent warming more closely in the tropics. bioRxiv [Preprint]. doi: 10.1101/2020.05.18.102848
Fritsche, S., Klocko, A. L., Boron, A., Brunner, A. M., and Thorlby, G. (2018). Strategies for engineering reproductive sterility in plantation forests. Front. Plant Sci. 9:1671. doi: 10.3389/fpls.2018.01671
Galeano, C. H., Cortés, A. J., Fernandez, A. C., Soler, A., Franco-Herrera, N., Makunde, G., et al. (2012). Gene-based single nucleotide polymorphism markers for genetic and association mapping in common bean. BMC Genet. 13:48. doi: 10.1186/1471-2156-13-48
Gianola, D., Fernando, R. L., and Stella, A. (2006). Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173, 1761–1776. doi: 10.1534/genetics.105.049510
Gonzalez, A., Germain, R. M., Srivastava, D. S., Filotas, E., Dee, L. E., Gravel, D., et al. (2020). Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776.
Grattapaglia, D., Silva-Junior, O. B., Resende, R. T., Cappa, E. P., Muller, B. S. F., Tan, B., et al. (2018). Quantitative genetics and genomics converge to accelerate forest tree breeding. Front. Plant Sci. 9:1693. doi: 10.3389/fpls.2018.01693
Hallingback, H. R., Berlin, S., Nordh, N. E., Weih, M., and Ronnberg-Wastljung, A. C. (2019). Genome wide associations of growth, phenology, and plasticity traits in willow [Salix Viminalis (L.)]. Front. Plant Sci. 10:753. doi: 10.3389/fpls.2019.00753
Herzog, E., and Frisch, M. (2011). Selection strategies for marker-assisted backcrossing with high-throughput marker systems. Theor. Appl. Genet. 123, 251–260. doi: 10.1007/s00122-011-1581-0
Hickey, J. M., Chiurugwi, T., Mackay, I., Powell, W., and Implementing Genomic Selection in Cgiar Breeding Programs Workshop Participants (2017). Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat. Genet. 49, 1297–1303. doi: 10.1038/ng.3920
Hipp, A. L., Manos, P. S., Hahn, M., Avishai, M., Bodenes, C., Cavender-Bares, J., et al. (2020). Genomic landscape of the global oak phylogeny. New Phytol. 226, 1198–1212. doi: 10.1111/nph.16162
Holliday, J. A., Aitken, S. N., Cooke, J. E., Fady, B., González-Martínez, S. C., Heuertz, M., et al. (2017). Advances in ecological genomics in forest trees and applications to genetic resources conservation and breeding. Mol. Ecol. 26, 706–717. doi: 10.1111/mec.13963
Holliday, J. A., Suren, H., and Aitken, S. N. (2011). Divergent selection and heterogeneous migration rates across the range of sitka spruce (Picea Sitchensis). Proc. Biol. Sci. 279, 1675–1683. doi: 10.1098/rspb.2011.1805
Holliday, J. A., Zhou, L., Bawa, R., Zhang, M., and Oubida, R. W. (2016). Evidence for extensive parallelism but divergent genomic architecture of adaptation along altitudinal and latitudinal gradients in Populus trichocarpa. New Phytol. 209, 1240–1251. doi: 10.1111/nph.13643
Howe, G. T., and Brunner, A. M. (2005). An evolving approach to understanding plant adaptation. New Phytol. 167, 1–5. doi: 10.1111/j.1469-8137.2005.01469.x
Ingvarsson, P. K., and Bernhardsson, C. (2020). Genome-wide signatures of environmental adaptation in European Aspen (Populus Tremula) under current and future climate conditions. Evol. Appl. 13, 132–142. doi: 10.1111/eva.12792
Ingvarsson, P. K., and Dahlberg, H. (2018). The effects of clonal forestry on genetic diversity in wild and domesticated stands of forest trees. Scand. J. For. Res. 34, 370–379. doi: 10.1080/02827581.2018.1469665
Ingvarsson, P. K., Hvidsten, T. R., and Street, N. R. (2016). Towards integration of population and comparative genomics in forest trees. New Phytol. 212, 338–344. doi: 10.1111/nph.14153
Isabel, N., Holliday, J. A., and Aitken, S. N. (2020). Forest genomics: advancing climate adaptation, forest health, productivity, and conservation. Evol. Appl. 13, 3–10. doi: 10.1111/eva.12902
Karrenberg, S., Buerkle, C. A., Field, D. L., and Savolainen, V. (2020). Dedication: Christian Lexer (1971-2019). Philos. Trans. R. Soc. Lond. B Biol. Sci. 375:20200232. doi: 10.1098/rstb.2020.0232
Kehel, Z., Sanchez-Garcia, M., El Baouchi, A., Aberkane, H., Tsivelikas, A., Charles, C., et al. (2020). Predictive characterization for seed morphometric traits for genebank accessions using genomic selection. Front. Ecol. Evol. 8:32. doi: 10.3389/fevo.2020.00032
Kelleher, C. T., Wilkin, J., Zhuang, J., Cortés, A. J., Quintero, ÁL. P., Gallagher, T. F., et al. (2012). Snp discovery, gene diversity, and linkage disequilibrium in wild populations of Populus tremuloides. Tree Genet. Genomes 821–829. doi: 10.1007/s11295-012-0467-x
Khan, M. A., and Korban, S. S. (2012). Association mapping in forest trees and fruit crops. J. Exp. Bot. 63, 4045–4060. doi: 10.1093/jxb/ers105
Klocko, A. L., Brunner, A. M., Huang, J., Meilan, R., Lu, H., Ma, C., et al. (2016). Containment of transgenic trees by suppression of leafy. Nat. Biotechnol. 34, 918–922. doi: 10.1038/nbt.3636
Kremer, A., and Hipp, A. L. (2020). Oaks: an evolutionary success story. New Phytol. 226, 987–1011. doi: 10.1111/nph.16274
Kremer, A., Potts, B. M., Delzon, S., and Bailey, J. (2014). Genetic divergence in forest trees: understanding the consequences of climate change. Funct. Ecol. 28, 22–36. doi: 10.1111/1365-2435.12169
Lafon-Placette, C., Vallejo-Marín, M., Parisod, C., Abbott, R. J., and Köhler, C. (2016). Current plant speciation research: unravelling the processes and mechanisms behind the evolution of reproductive isolation barriers. New Phytol. 209, 29–33. doi: 10.1111/nph.13756
Lascoux, M., Glémin, S., and Savolainen, O. (2016). Local adaptation in plants. Encycl. Life Sci. 0025270, 1–7. doi: 10.1002/9780470015902.a0025270
Lenoir, J., Gegout, J. C., Marquet, P. A., De Ruffray, P., and Brisse, H. (2008). A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771. doi: 10.1126/science.1156831
Lenz, P. R. N., Nadeau, S., Mottet, M. J., Perron, M., Isabel, N., Beaulieu, J., et al. (2020). Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway Spruce. Evol. Appl. 13, 76–94. doi: 10.1111/eva.12823
Leroy, T., Louvet, J. M., Lalanne, C., Le Provost, G., Labadie, K., Aury, J. M., et al. (2020a). Adaptive introgression as a driver of local adaptation to climate in European white oaks. New Phytol. 226, 1171–1182. doi: 10.1111/nph.16095
Leroy, T., Plomion, C., and Kremer, A. (2020b). Oak symbolism in the light of genomics. New Phytol. 226, 1012–1017. doi: 10.1111/nph.15987
Leroy, T., Rougemont, Q., Dupouey, J. L., Bodenes, C., Lalanne, C., Belser, C., et al. (2020c). Massive postglacial gene flow between European white oaks uncovered genes underlying species barriers. New Phytol. 226, 1183–1197. doi: 10.1111/nph.16039
Li, Y., Klápště, J., Telfer, E., Wilcox, P., Graham, N., Macdonald, L., et al. (2019). Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 20:1026. doi: 10.1186/s12864-019-6420-8
Libbrecht, M. W., and Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332. doi: 10.1038/nrg3920
Lind, B. M., Menon, M., Bolte, C. E., Faske, T. M., and Eckert, A. J. (2018). The genomics of local adaptation in trees: are we out of the woods yet? Tree Genet. Genomes 14:29.
Little, C. J., Wheeler, J. A., Sedlacek, J., Cortés, A. J., and Rixen, C. (2016). Small-scale drivers: the importance of nutrient availability and snowmelt timing on performance of the alpine shrub Salix Herbacea. Oecologia 180, 1015–1024. doi: 10.1007/s00442-015-3394-3
Liu, S., Cornille, A., Decroocq, S., Tricon, D., Chague, A., Eyquard, J. P., et al. (2019). The complex evolutionary history of apricots: species divergence, gene flow and multiple domestication events. Mol. Ecol. 28, 5299–5314. doi: 10.1111/mec.15296
López-Hernández, F., and Cortés, A. J. (2019). Last-generation genome–environment associations reveal the genetic basis of heat tolerance in common bean (Phaseolus vulgaris L.). Front. Genet. 10:22. doi: 10.3389/fgene.2019.00954
Madriñán, S., Cortés, A. J., and Richardson, J. E. (2013). Páramo is the world’s fastest evolving and coolest biodiversity hotspot. Front. Genet. 4:192. doi: 10.3389/fgene.2013.00192
Mahony, C. R., Maclachlan, I. R., Lind, B. M., Yoder, J. B., Wang, T., and Aitken, S. N. (2020). Evaluating genomic data for management of local adaptation in a changing climate: a lodgepole pine case study. Evol. Appl. 13, 116–131. doi: 10.1111/eva.12871
Marques, D. A., Meier, J. I., and Seehausen, O. (2019). A combinatorial view on speciation and adaptive radiation. Trends Ecol. Evol. 34, 531–544. doi: 10.1016/j.tree.2019.02.008
Martín, M. A., Herrera, M. A., and MartiìN, L. M. (2012). In situ conservation and landscape genetics in forest species. J. Nat. Resour. Dev. 2, 1–5.
Mason, A. S., and Wendel, J. F. (2020). Homoeologous exchanges, segmental allopolyploidy, and polyploid genome evolution. Front. Genet. 11:1014. doi: 10.3389/fgene.2020.01014
Mattioni, C., Martin, M. A., Chiocchini, F., Gaudet, M., Pollegioni, P., Velichkov, I., et al. (2017). Landscape genetics structure of european sweet chestnut (Castanea Sativa Mill): indications for conservation priorities. Tree Genet. Genomes 13:39.
Mayol, M., Riba, M., Cavers, S., Grivet, D., Vincenot, L., Cattonaro, F., et al. (2020). A multiscale approach to detect selection in nonmodel tree species: widespread adaptation despite population decline in Taxus baccata L. Evol. Appl. 13, 143–160. doi: 10.1111/eva.12838
McKown, A. D., Guy, R. D., Quamme, L., Klapste, J., La Mantia, J., Constabel, C. P., et al. (2014). Association genetics, geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and disease resistance trade-offs. Mol. Ecol. 23, 5771–5790. doi: 10.1111/mec.12969
McKown, A. D., Klapste, J., Guy, R. D., El-Kassaby, Y. A., and Mansfield, S. D. (2018). Ecological genomics of variation in bud-break phenology and mechanisms of response to climate warming in Populus trichocarpa. New Phytol. 220, 300–316. doi: 10.1111/nph.15273
Meilan, R., Brunner, A. M., Skinnera, J. S., and Strauss, S. H. (2001). Modification of flowering in transgenic trees. Prog. Biotechnol. 18, 247–256.
Meuwissen, T. H. E., Hayes, B. J., and Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Meyer, F. E., Shuey, L. S., Naidoo, S., Mamni, T., Berger, D. K., Myburg, A. A., et al. (2016). Dual Rna-sequencing of Eucalyptus nitens during Phytophthora cinnamomi challenge reveals pathogen and host factors influencing compatibility. Front. Plant Sci. 7:191. doi: 10.3389/fpls.2016.00191
Migicovsky, Z., and Myles, S. (2017). Exploiting wild relatives for genomics-assisted breeding of perennial crops. Front. Plant Sci. 8:460. doi: 10.3389/fpls.2017.00460
Muranty, H., Jorge, V., Bastien, C., Lepoittevin, C., Bouffier, L., and Sanchez, L. (2014). Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of mas in crops. Tree Genet. Genomes 10, 1491–1510. doi: 10.1007/s11295-014-0790-5
Myburg, A. A., Hussey, S. G., Wang, J. P., Street, N. R., and Mizrachi, E. (2019). Systems and synthetic biology of forest trees: a bioengineering paradigm for Woody biomass feedstocks. Front. Plant Sci. 10:775. doi: 10.3389/fpls.2019.00775
Naidoo, S., Külheim, C., Zwart, L., Mangwanda, R., Oates, C. N., Visser, E. A., et al. (2014). Uncovering the defence responses of Eucalyptus to pests and pathogens in the genomics age. Tree Physiol. 34, 931–943. doi: 10.1093/treephys/tpu075
Naidoo, S., Slippers, B., Plett, J. M., Coles, D., and Oates, C. N. (2019). The road to resistance in forest trees. Front. Plant Sci. 10:273. doi: 10.3389/fpls.2019.00273
Neale, D. B., and Kremer, A. (2011). Forest tree genomics: growing resources and applications. Nat. Rev. Genet. 12, 111–122. doi: 10.1038/nrg2931
Neale, D. B., and Savolainen, O. (2004). Association genetics of complex traits in conifers. Trends Plant Sci. 9, 325–330. doi: 10.1016/j.tplants.2004.05.006
Nieto Feliner, G., Casacuberta, J., and Wendel, J. F. (2020). Genomics of evolutionary novelty in hybrids and polyploids. Front. Genet. 11:792. doi: 10.3389/fgene.2020.00792
Nystedt, B., Street, N. R., Wetterbom, A., Zuccolo, A., Lin, Y.-C., Scofield, D. G., et al. (2013). The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584.
Pennisi, E. (2020). Tropical forests store carbon despite warming. Science 368:813. doi: 10.1126/science.368.6493.813
Pereira-Lorenzo, S., Ramos-Cabrer, A. M., Barreneche, T., Mattioni, C., Villani, F., Díaz-Hernández, B., et al. (2019). Instant domestication process of European chestnut cultivars. Ann. Appl. Biol. 174, 74–85. doi: 10.1111/aab.12474
Phillips, J., Ramirez, S., Wayson, C., and Duque, A. (2019). Differences in carbon stocks along an elevational gradient in tropical mountain forests of Colombia. Biotropica 51, 490–499. doi: 10.1111/btp.12675
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., and Blair, M. E. (2017). Opening the black box: an open-source release of maxent. Ecography 40, 887–893. doi: 10.1111/ecog.03049
Piot, A., Prunier, J., Isabel, N., Klapste, J., El-Kassaby, Y. A., Villarreal Aguilar, J. C., et al. (2019). Genomic diversity evaluation of Populus trichocarpa germplasm for rare variant genetic association studies. Front. Genet. 10:1384. doi: 10.3389/fgene.2019.01384
Plomion, C., Aury, J. M., Amselem, J., Leroy, T., Murat, F., Duplessis, S., et al. (2018). Oak genome reveals facets of long lifespan. Nat. Plants 4, 440–452.
Plomion, C., and Martin, F. (2020). Oak genomics is proving its worth. New Phytol. 226, 943–946. doi: 10.1111/nph.16560
Pluess, A. R., Frank, A., Heiri, C., Lalague, H., Vendramin, G. G., and Oddou-Muratorio, S. (2016). Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica. New Phytol. 210, 589–601. doi: 10.1111/nph.13809
Ratcliffe, B., Thistlethwaite, F., El-Dien, O. G., Cappa, E. P., Porth, I., Klápštì, J., et al. (2019). Inter- and intra-generation genomic predictions for Douglas-Fir growth in unobserved environments. bioRxiv [Preprint]. doi: 10.1101/540765
Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M., and Holderegger, R. (2015). A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24, 4348–4370. doi: 10.1111/mec.13322
Resende, M. D. V., Resende, M. F. R., Sansaloni, C. P., Petroli, C. D., Missiaggia, A. A., and Aguiar, A. M. (2012). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194, 116–128. doi: 10.1111/j.1469-8137.2011.04038.x
Resende, M. F., Muñoz, P., Resende, M. D., Garrick, D. J., Fernando, R. L., Davis, J. M., et al. (2012). Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190, 1503–1510. doi: 10.1534/genetics.111.137026
Resende, R. T., Piepho, H. P., Rosa, G. J. M., Silva-Junior, O. B., Silva, F. F., Resende, M. D. V., et al. (2020). Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. Theor. Appl. Genet. doi: 10.1007/s00122-020-03684-z
Roudbar, M. A., Momen, M., Mousavi, S. F., Ardestani, S. S., Lopes, F. B., Gianola, D., et al. (2020). Genome-wide methylation prediction of biological age using reproducing Kernel Hilbert spaces and Bayesian ridge regressions. bioRxiv [Preprint]. doi: 10.1101/2020.08.25.266924
Savolainen, O., Lascoux, M., and Merilä, J. (2013). Ecological genomics of local adaptation. Nat. Rev. Genet. 14, 807–820. doi: 10.1038/nrg3522
Sawitri, S., Tani, N., Na’iem, M., Widiyatno, Indrioko, S., Uchiyama, K., et al. (2020). Potential of genome-wide association studies and genomic selection to improve productivity and quality of commercial timber species in tropical rainforest, a case study of Shorea platyclados. Forests 11:239. doi: 10.3390/f11020239
Scherer, L., Svenning, J. C., Huang, J., Seymour, C. L., Sandel, B., Mueller, N., et al. (2020). Global priorities of environmental issues to combat food insecurity and biodiversity loss. Sci. Total Environ. 730:139096. doi: 10.1016/j.scitotenv.2020.139096
Schilthuizen, M., Hoekstra, R. F., and Gittenberger, E. (2004). Hybridization, rare alleles and adaptive radiation. Trends Ecol. Evol. 19, 404–405. doi: 10.1016/j.tree.2004.06.005
Schloötterer, C. (2020). Christian Lexer: a lifelong passion for trees. Mol. Ecol. 29, 443–444. doi: 10.1111/mec.15363
Schrider, D. R., and Kern, A. D. (2018). Supervised machine learning for population genetics: a new paradigm. Trends Genet. 34, 301–312. doi: 10.1016/j.tig.2017.12.005
Scott, M. F., Ladejobi, O., Amer, S., Bentley, A. R., Biernaskie, J., Boden, S. A., et al. (2020). Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity. doi: 10.1038/s41437-020-0336-6 [Epub ahead of print].
Sedlacek, J., Bossdorf, O., Cortés, A. J., Wheeler, J. A., and Van-Kleunen, M. (2014). What role do plant-soil interactions play in the habitat suitability and potential range expansion of the alpine dwarf shrub Salix herbacea? Basic Appl. Ecol. 15, 305–315. doi: 10.1016/j.baae.2014.05.006
Sedlacek, J., Cortés, A. J., Wheeler, J. A., Bossdorf, O., Hoch, G., Klapste, J., et al. (2016). Evolutionary potential in the alpine: trait heritabilities and performance variation of the dwarf willow Salix herbacea from different elevations and microhabitats. Ecol. Evol. 6, 3940–3952. doi: 10.1002/ece3.2171
Sedlacek, J., Wheeler, J. A., Cortés, A. J., Bossdorf, O., Hoch, G., Lexer, C., et al. (2015). The response of the alpine dwarf shrub Salix herbacea to altered snowmelt timing: lessons from a multi-site transplant experiment. PLoS One 10:e0122395. doi: 10.1371/journal.pone.0122395
Seehausen, O. (2004). Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207. doi: 10.1016/j.tree.2004.01.003
Sentinella, A. T., Warton, D. I., Sherwin, W. B., Offord, C. A., Moles, A. T., and Wang, Z. (2020). Tropical plants do not have narrower temperature tolerances, but are more at risk from warming because they are close to their upper thermal limits. Glob. Ecol. Biogeogr. 29, 1387–1398. doi: 10.1111/geb.13117
Shang, H., Hess, J., Pickup, M., Field, D. L., Ingvarsson, P. K., Liu, J., et al. (2020). Evolution of strong reproductive isolation in plants: broad-scale patterns and lessons from a perennial model group. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375:20190544. doi: 10.1098/rstb.2019.0544
Sousa, T. V., Caixeta, E. T., Alkimim, E. R., Oliveira, A. C. B., Pereira, A. A., Sakiyama, N. S., et al. (2018). Early selection enabled by the implementation of genomic selection in Coffea arabica breeding. Front. Plant Sci. 9:1934. doi: 10.3389/fpls.2018.01934
Souza, L. M., Francisco, F. R., Goncalves, P. S., Scaloppi Junior, E. J., Le Guen, V., Fritsche-Neto, R., et al. (2019). Genomic selection in rubber tree breeding: a comparison of models and methods for managing GxE interactions. Front. Plant Sci. 10:1353. doi: 10.3389/fpls.2019.01353
Sow, M. D., Allona, I., Ambroise, C., Conde, D., Fichot, R., Gribkova, S., et al. (2018). Epigenetics in forest trees: state of the art and potential implications for breeding and management in a context of climate change. Adv. Bot. Res. 88, 387–453. doi: 10.1016/bs.abr.2018.09.003
Steinbauer, M. J., Grytnes, J. A., Jurasinski, G., Kulonen, A., Lenoir, J., Pauli, H., et al. (2018). Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234.
Stocks, J. J., Metheringham, C. L., Plumb, W. J., Lee, S. J., Kelly, L. J., Nichols, R. A., et al. (2019). Genomic basis of European ash tree resistance to ash dieback fungus. Nat. Ecol. Evol. 3, 1686–1696. doi: 10.1038/s41559-019-1036-6
Stölting, K. N., Paris, M., Meier, C., Heinze, B., Castiglione, S., Bartha, D., et al. (2015). Genome-wide patterns of differentiation and spatially varying selection between postglacial recolonization lineages of Populus Alba (Salicaceae), a widespread forest tree. New Phytol. 207, 723–734. doi: 10.1111/nph.13392
Sullivan, M., Lewis, S. L., Affum-Baffoe, K., Castilho, C., Costa, F., Sanchez, A. C., et al. (2020). Long-term thermal sensitivity of earth’s tropical forests. Science 368, 869–874.
Suontama, M., Klápště, J., Telfer, E., Graham, N., Stovold, T., Low, C., et al. (2019). Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity 122, 370–379. doi: 10.1038/s41437-018-0119-5
Tan, B., Grattapaglia, D., Martins, G. S., Ferreira, K. Z., Sundberg, B. R., and Ingvarsson, P. R. K. (2017). Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids. BMC Plant Biol. 17:110. doi: 10.1186/s12870-017-1059-6
Technow, F., Schrag, T. A., Schipprack, W., Bauer, E., Simianer, H., and Melchinger, A. E. (2014). Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197, 1343–1355. doi: 10.1534/genetics.114.165860
Thistlethwaite, F. R., Gamal El-Dien, O., Ratcliffe, B., Klapste, J., Porth, I., Chen, C., et al. (2020). Linkage Disequilibrium Vs. Pedigree: genomic selection prediction accuracy in conifer species. PLoS One 15:e0232201. doi: 10.1371/journal.pone.0232201
Thistlethwaite, F. R., Ratcliffe, B., Klápštì, J., Porth, I., Chen, C., Stoehr, M. U., et al. (2017). Genomic prediction accuracies in space and time for height and wood density of Douglas-Fir using exome capture as the genotyping platform. BMC Genomics 18:930. doi: 10.1186/s12864-017-4258-5
Thistlethwaite, F. R., Ratcliffe, B., Klápštì, J., Porth, I., Chen, C., Stoehr, M. U., et al. (2019a). Genomic selection of juvenile height across a single-generational gap in Douglas-Fir. Heredity 122, 848–863. doi: 10.1038/s41437-018-0172-0
Thistlethwaite, F. R., Ratcliffe, B., Klápštì, J., Porth, I., Chen, C., Stoehr, M. U., et al. (2019b). Genomic selection of juvenile height across a single-generational gap in Douglas-Fir. Heredity 122, 848–863. doi: 10.1038/s41437-018-0172-0
Tito, R., Vasconcelos, H. L., and Feeley, K. J. (2020). Mountain ecosystems as natural laboratories for climate change experiments. Front. For. Glob. Change 3:38. doi: 10.3389/ffgc.2020.00038
Tuskan, G. A., Groover, A. T., Schmutz, J., Difazio, S. P., Myburg, A., Grattapaglia, D., et al. (2018). Hardwood tree genomics: unlocking woody plant biology. Front. Plant Sci. 9:1799. doi: 10.3389/fpls.2018.01799
Ukrainetz, N. K., and Mansfield, S. D. (2020). Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet. Genomes 16:14.
Ulian, T., Diazgranados, M., Pironon, S., Padulosi, S., Liu, U., Davies, L., et al. (2020). Unlocking plant resources to support food security and promote sustainable agriculture. Plants People Planet 2, 421–445. doi: 10.1002/ppp3.10145
Valencia, J. B., Mesa, J., León, J. G., Madriñán, S., and Cortés, A. J. (2020). Climate vulnerability assessment of the Espeletia complex on PaìRamo sky islands in the Northern Andes. Front. Ecol. Evol. 8:565708. doi: 10.3389/fevo.2020.565708
Wang, J., Street, N. R., Park, E. J., Liu, J., and Ingvarsson, P. K. (2020). Evidence for widespread selection in shaping the genomic landscape during speciation of Populus. Mol. Ecol. 29, 1120–1136. doi: 10.1111/mec.15388
Wang, M., Zhang, L., Zhang, Z., Li, M., Wang, D., Zhang, X., et al. (2020). Phylogenomics of the genus Populus reveals extensive interspecific gene flow and balancing selection. New Phytol. 225, 1370–1382. doi: 10.1111/nph.16215
Westbrook, J. W., Holliday, J. A., Newhouse, A. E., and Powell, W. A. (2019). A plan to diversify a transgenic blight-tolerant American chestnut population using citizen science. Plants People Planet 2, 84–95. doi: 10.1002/ppp3.10061
Westbrook, J. W., Zhang, Q., Mandal, M. K., Jenkins, E. V., Barth, L. E., Jenkins, J. W., et al. (2020). Optimizing genomic selection for blight resistance in American chestnut backcross populations: a trade-off with American chestnut ancestry implies resistance is polygenic. Evol. Appl. 13, 31–47. doi: 10.1111/eva.12886
Wheeler, J. A., Cortés, A. J., Sedlacek, J., Karrenberg, S., Van Kleunen, M., Wipf, S., et al. (2016). The snow and the willows: accelerated spring snowmelt reduces performance in the low-lying alpine shrub Salix herbacea. J. Ecol. 104, 1041–1050. doi: 10.1111/1365-2745.12579
Wheeler, J. A., Hoch, G., Cortés, A. J., Sedlacek, J., Wipf, S., and Rixen, C. (2014). Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175, 219–229. doi: 10.1007/s00442-013-2872-8
Wheeler, J. A., Schnider, F., Sedlacek, J., Cortés, A. J., Wipf, S., Hoch, G., et al. (2015). With a little help from my friends: community facilitation increases performance in the dwarf shrub Salix herbacea. Basic Appl. Ecol. 16, 202–209. doi: 10.1016/j.baae.2015.02.004
Yeaman, S., Hodgins, K. A., Lotterhos, K. E., Suren, H., Nadeau, S., Degner, J. C., et al. (2016). Convergent local adaptation to climate in distantly related conifers. Science 353, 1431–1433. doi: 10.1126/science.aaf7812
Zahn, L. M., and Purnell, B. A. (2016). Genes under pressure. Science 354:52. doi: 10.1126/science.354.6308.52
Zhou, L., Bawa, R., and Holliday, J. A. (2014). Exome resequencing reveals signatures of demographic and adaptive processes across the genome and range of black cottonwood (Populus trichocarpa). Mol. Ecol. 23, 2486–2499. doi: 10.1111/mec.12752
Keywords: genomics of adaptation, genomic prediction, genome-wide association studies, genome-wide selection scans, assisted gene flow, machine learning, big data
Citation: Cortés AJ, Restrepo-Montoya M and Bedoya-Canas LE (2020) Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate. Front. Plant Sci. 11:583323. doi: 10.3389/fpls.2020.583323
Received: 14 July 2020; Accepted: 29 September 2020;
Published: 21 October 2020.
Edited by:
Sanushka Naidoo, University of Pretoria, South AfricaReviewed by:
Dejun Li, Chinese Academy of Tropical Agricultural Sciences, ChinaRodrigo J. Hasbun, University of Concepcion, Chile
Copyright © 2020 Cortés, Restrepo-Montoya and Bedoya-Canas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Andrés J. Cortés, YWNvcnRlc0BhZ3Jvc2F2aWEuY28=