in

Adaptive evolution in a conifer hybrid zone is driven by a mosaic of recently introgressed and background genetic variants

  • 1.

    Abbott, R. D. et al. Hybridization and speciation. J. Evol. Bio. 26, 229–246 (2013).

    CAS  Article  Google Scholar 

  • 2.

    de Lafontaine, G. & Bousquet, J. Asymmetry matters: a genomic assessment of directional biases in gene flow between hybridizing spruces. Ecol. Evol. 7, 3883–3893 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  • 3.

    Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 4.

    Anderson, E. & Stebbins, G. L. Hybridization as an evolutionary stimulus. Evolution 8, 378–388 (1954).

    Article  Google Scholar 

  • 5.

    De La Torre, A. R., Li, Z., Van de Peer, Y. & Ingvarsson, P. K. Contrasting rates of molecular evolution and patterns of selection among gymnosperms and flowering plants. Mol. Bio. Evol. 34, 1363–1377 (2017).

    Article  CAS  Google Scholar 

  • 6.

    Critchfield, W. B. Hybridization and classification of the white pines (Pinus section Strobus). Taxon 35, 647–656 (1986).

    Article  Google Scholar 

  • 7.

    Nystedt, B. et al. The Norway spruce genome sequence and conifer genome evolution. Nature 497, 579–584 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 8.

    Bouille, M. & Bousquet, J. Trans-species shared polymorphisms at orthologous nuclear gene loci among distant species in the conifer Picea (Pinaceae): implications for long term maintenance of genetic diversity in trees. Am. J. Bot. 92, 63–73 (2005).

    PubMed  Article  PubMed Central  Google Scholar 

  • 9.

    Hamilton, J. A., Lexer, C. & Aitken, S. N. Genomic and phenotypic architecture of a spruce hybrid zone (Picea sitchensis × P. glauca). Mol. Ecol. 22, 827–841 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 10.

    Hamilton, J. & Miller, J. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  • 11.

    Jagoda, E. et al. Disentangling immediate adaptive introgression from selection on standing introgressed variation in humans. Mol. Biol. Evol. 35, 623–630 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 12.

    Bresadola, L. et al. Admixture mapping in interspecific Populus hybrids identifies classes of genomic architectures for phytochemical, morphological and growth traits. N. Phytol. 223, 2076–2089 (2019).

    CAS  Article  Google Scholar 

  • 13.

    Suarez-Gonzalez, A. et al. Genomic and functional approaches reveal a case of adaptive introgression from Populus balsamifera (balsam poplar) in P. trichocarpa (black cottonwood). Mol. Ecol. 25, 2427–2442 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 14.

    Suarez-Gonzalez, A., Hefer, C. A., Lexer, C., Cronk, Q. C. & Douglas, C. J. Scale and direction of adaptive introgression between black cottonwood (Populus trichocarpa) and balsam poplar (P. balsamifera). Mol. Ecol. 27, 1667–1680 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 15.

    Leroy, T. et al. Adaptive introgression as a driver of local adaptation to climate in European white oaks. New. Phytol. 226, 1171–1182 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 16.

    Hufford, M.B. et al. Genomic signature of crop-wild introgression in Maize. PLoS Genet. 9, e100347 (2013).

    Article  Google Scholar 

  • 17.

    Ma, Y. et al. Ancient introgression drives adaptation to cooler and drier mountain habitats in a cypress species complex. Commun. Biol. 18, 210–213 (2019).

    Google Scholar 

  • 18.

    Pyhäjärvi, T., Hufford, M. B., Mezmouk, S. & Ross-Ibarra, J. Complex patterns of local adaptation in Teosinte. Genome Biol. Evol. 5, 1594–1609 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 19.

    Mei, W., Stetter, M. G. & Stitzer, M. C. Adaptation in plant genomes: bigger is different. Am. J. Bot. 105, 16–19 (2019).

    Article  Google Scholar 

  • 20.

    Syring, J. et al. Widespread genealogical non-monophyly in species of the Pinus subgenus. Strobus. Syst. Biol. 56, 163–181 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 21.

    Menon, M. et al. The role of hybridization during ecological divergence of southwestern white pine (Pinus strobiformis) and limber pine (P. flexilis). Mol. Ecol. 27, 1245–1260 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  • 22.

    Looney, C. E. & Waring, K. M. Pinus strobiformis (southwestern white pine) stand dynamics, regeneration, and disturbance ecology: a review. For. Ecol. Manag. 287, 90–102 (2013).

    Article  Google Scholar 

  • 23.

    Schoettle, A. W. & Rochelle, S. G. Morphological variation of Pinus flexilis (Pinaceae), a bird-dispersed pine, across a range of elevations. Am. J. Bot. 87, 1797–1806 (2000).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 24.

    Frankis, M. P. The high altitude white pines (Pinus L. subgenus Strobus Lemmon, Pinaceae) of Mexico and the adjacent SW USA. Int. Dendrol. Soc. Yearb. 2008, 64–72 (2009).

    Google Scholar 

  • 25.

    Tomback, D. F. et al. Seed dispersal in limber and southwestern white pine: comparing core and peripheral populations. In The Future of High Elevation, Five-Needle White Pines in Western North America: Proceedings of the High Five Symposium. Proceedings RMRS-P- 63 69–71 (US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, 2011).

  • 26.

    Moreno-Letelier, A., Ortíz-Medrano, A. & Piñero, D. Niche divergence versus neutral processes: combined environmental and genetic analyses identify contrasting patterns of differentiation in recently diverged pine species. PLoS ONE 8, e78228 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 27.

    Moreno-Letelier, A. & Barraclough, T. G. Mosaic genetic differentiation along environmental and geographic gradients indicate divergent selection in a white pine species complex. Evol. Ecol. 29, 733–748 (2015).

    Article  Google Scholar 

  • 28.

    Little, E. L. Jr. Atlas of United States Trees. Vol. 5, 22 (Florida. Misc. Publ. 1361, U.S. Department of Agriculture, Forest Service, 1978).

  • 29.

    Bisbee, J. Cone morphology of the Pinus ayacahuite-flexilis complex of the southwestern United States and Mexico. Bull. Cupressus Conserv. Proj. 3, 3–33 (2014).

    Google Scholar 

  • 30.

    Borgman, E. M., Schoettle, A. W. & Angert, A. L. Assessing the potential for maladaptation during active management of limber pine populations: a common garden study detects genetic differentiation in response to soil moisture in the Southern Rocky Mountains. Can. J. For. Res. 45, 496–505 (2015).

    CAS  Article  Google Scholar 

  • 31.

    Neale, D. B. & Kremer, A. Forest tree genomics: growing resources and applications. Nat. Rev. Genet. 12, 111–122 (2011).

    CAS  PubMed  Article  Google Scholar 

  • 32.

    Mitton, J., Kreiser, B. R. & Latta, R. G. Glacial refugia of limber pine (Pinus flexilis James) inferred from the population structure of mitochondrial DNA. Mol. Ecol. 9, 91–97 (2000).

    CAS  PubMed  Article  Google Scholar 

  • 33.

    Jorgensen, S., Hamrick, J. L. & Wells, P. V. Regional patterns of genetic diversity in Pinus flexilis (Pinaceae) reveal complex species history. Am. J. Bot. 89, 792–800 (2002).

    PubMed  Article  Google Scholar 

  • 34.

    Goodrich, B. A., Waring, K. M. & Kolb, T. E. Genetic variation in Pinus strobiformis growth and drought tolerance from southwestern US populations. Tree Physiol. 36, 1219–1235 (2016).

    CAS  PubMed  Article  Google Scholar 

  • 35.

    DaBell, J. Pinus Strobiformis Response to an Elevational Gradient and Correlation with Source Climate. Master’s thesis, Northern Arizona University (2017).

  • 36.

    Francis, J. A. & Vavrus, S. J. Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett. 10, 014005 (2015).

    Article  Google Scholar 

  • 37.

    Rellstab, C. et al. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  • 38.

    Coop, G., Witonsky, D., Di Rienzo, A. & Pritchard, J. K. Using environmental correlations to identify loci underlying local adaptation. Genetics 185, 1411–1423 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 39.

    Levitt J. Responses of Plants to Environmental Stress. Chilling, Freezing, and High Temperature Stresses 2nd edn (Academic Press, 1980).

  • 40.

    Bierne, N., Welch, J., Loire, E., Bonhomme, F. & David, P. The coupling hypothesis: why genome scans may fail to map local adaptation genes. Mol. Ecol. 20, 2044–2072 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  • 41.

    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. 1, 3–14 (2010).

    Article  Google Scholar 

  • 42.

    Harrison, K. A. et al. Signatures of polygenic adaptation associated with climate across the range of a threatened fish species with high genetic connectivity. Mol. Ecol. 26, 6253–6269 (2017).

    Article  Google Scholar 

  • 43.

    Lind, B. M. et al. Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Mol. Ecol. 26, 3168–3185 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  • 44.

    Csillery, K. et al. Detecting short spatial scale local adaptation and epistatic selection in climate‐related candidate genes in European beech (Fagus sylvatica) populations. Mol. Ecol. 23, 4696–4708 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 45.

    Schumer, M. & Brandvain, Y. Determining epistatic selection in admixed populations. Mol. Ecol. 25, 2577–2591 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  • 46.

    Menon, M. et al. Tracing the footprints of a moving hybrid zone under a demographic history of speciation with gene flow. Evol. Appl. 13, 195–209 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 47.

    Whitney, K. D. et al. Quantitative trait locus mapping identifies candidate alleles involved in adaptive introgression and range expansion in a wild sunflower. Mol. Ecol. 24, 2194–2211 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 48.

    Chhatre, V. E., Evan, L. M., DiFazio, S. P. & Keller, S. R. Adaptive introgression and maintenance of a trispecies hybrid complex in range‐edge populations of Populus. Mol. Ecol. 27, 4820–4838 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  • 49.

    Aitken, S. A. et al. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  • 50.

    Alberto, F. J. et al. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Chn. Bio. 19, 1645–1661 (2013).

    Article  Google Scholar 

  • 51.

    Kirkpatrick, M. & Barton, N. H. Evolution of a species’ range. Am. Nat. 150, 1–23 (1997).

    CAS  PubMed  Article  Google Scholar 

  • 52.

    Stebbins, G. L. The role of hybridization in evolution. Proc. Am. Philos. Soc. 103, 231–251 (1959).

    Google Scholar 

  • 53.

    Petit, R. J. & Excoffier, L. Gene flow and species delimitation. Trends Ecol. Evol. 24, 386–393 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  • 54.

    Barton, N. H. & Hewitt, G. M. Analysis of hybrid zones. Annu. Rev. Ecol. Evol. S 16, 113–148 (1985).

    Article  Google Scholar 

  • 55.

    Mimura, M., Mishima, M., Lascoux, M. & Yahara, T. Range shift and introgression of the rear and leading populations in two ecologically distinct Rubus species. BMC Evol. Biol. 2014, 209 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  • 56.

    De La Torre, A. R., Wang, T., Jaquish, B. & Aitken, S. N. Adaptation and exogenous selection in a Picea glauca × Picea engelmannii hybrid zone: implications for forest management under climate change. N. Phytol. 201, 687–699 (2014).

    Article  CAS  Google Scholar 

  • 57.

    Hamilton, J. R., De La Torre, A. R. & Aitken, S. N. Fine-scale environmental variation contributes to introgression in a three-species spruce hybrid complex. Tree Genet. Genomes 11, 1–14 (2015).

    Article  Google Scholar 

  • 58.

    Fraïsse, C. K., Belkhir, J., Welch, J. & Bierne, N. Local interspecies introgression is the main cause of extreme levels of intraspecific differentiation in mussels. Mol. Ecol. 25, 269–770 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  • 59.

    Wu, D. D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).

    PubMed  Article  PubMed Central  Google Scholar 

  • 60.

    Kremer, A. & Le Corre, V. Decoupling of differentiation between traits and their underlying genes in response to divergent selection. Heredity 10, 375–385 (2012).

    Article  Google Scholar 

  • 61.

    Eckert, A. J. et al. Local adaptation at fine spatial scales: an example from sugar pine (Pinus lambertiana, Pinaceae). Tree Genet. Genomes 11, 42 (2015).

    Article  Google Scholar 

  • 62.

    Hornoy, B., Pavy, N., Gérardi, S., Beaulieu, J. & Bousquet, J. Genetic adaptation to climate in white spruce involves small to moderate allele frequency shifts in functionally diverse genes. Genome Biol. Evol. 7, 3269–3285 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 63.

    Rieseberg, L. H. et al. Hybridization and the colonization of novel habitats by annual sunflowers. Genetica 129, 149–165 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  • 64.

    Lewontin, R. C. & Birch, L. C. Hybridization as a source of variation for adaptation to new environments. Evolution 20, 315–336 (1966).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 65.

    Pavy, N. et al. The heterogeneous levels of linkage disequilibrium in white spruce genes and comparative analysis with other conifers. Heredity 108, 273–284 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 66.

    Kim, B. Y., Huber, C. D. & Lohmueller, K. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 14, e1007741 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 67.

    Eyre-Walker, A., Woolfit, M. & Phelps, T. The distribution of fitness effects of new deleterious amino acid mutations in humans. Genetics 173, 891–900 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 68.

    Christe, C. et al. Adaptive evolution and segregating load contribute to the genomic landscape of divergence in two tree species connected by episodic gene flow. Mol. Ecol. 26, 59–76 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 69.

    Lu, M., Hodgins, K. A., Degner, J. C. & Yeaman, S. Purifying selection does not drive signatures of convergent local adaptation of lodgepole pine and interior spruce. BMC Evol. Biol. 19, 110 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 70.

    Whitlock, M. C. Temporal fluctuations in demographic parameters and the genetic variance among populations. Evolution 46, 608–615 (1992).

    PubMed  Article  Google Scholar 

  • 71.

    Lexer, C. et al. Genomic admixture analysis in European Populus spp. reveals unexpected patterns of reproductive isolation and mating. Genetics 186, 699–712 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 72.

    Lowry, D. et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17, 142–152 (2017).

    CAS  PubMed  Article  Google Scholar 

  • 73.

    Parchman, T. L. et al. RADseq approaches and applications for forest tree genetics. Tree Genet. Genomes 14, 39 (2018).

    Article  Google Scholar 

  • 74.

    Gossmann, T. I., Keightley, P. D. & Eyre-Walker, A. The effect of variation in the effective population size on the rate of adaptive molecular evolution in eukaryotes. Genome Biol. Evol. 4, 658–667 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  • 75.

    Lexer, C. & Widmer, A. The genic view of plant speciation: recent progress and emerging questions. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3023–3036 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  • 76.

    Bucholz, E. Early Growth, Water Relations and Growth: Common Garden Studies of Pinus Strobiformis under Climate Change. PhD dissertation, Northern Arizona University (2020).

  • 77.

    Lotterhos, K. & Whitlock, M. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).

    PubMed  Article  Google Scholar 

  • 78.

    Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 79.

    Goudet, J. hierfstat, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).

    Article  Google Scholar 

  • 80.

    R Core Team. R v.3.3.2: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  • 81.

    Parchman, T. L. et al. Genome -wide association genetics of an adaptive trait in lodgepole pine: association mapping of serotiny. Mol. Ecol. 21, 2991–3005 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 82.

    Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant -calling pipeline designed for population genomics of non -model organisms. PeerJ 2, e431 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  • 83.

    Wang, T., Hamann, A., Spittlehouse, D. L. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 84.

    Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  • 85.

    Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 86.

    Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).

    Google Scholar 

  • 87.

    Camacho et al. BLAST+: architecture and applications. BMC Bioinfo. 10, 421 (2009).

    Article  CAS  Google Scholar 

  • 88.

    Warnes, G., Gorjanc, G., Leisch, F. & Man, M. genetics: Population Genetics. R package version 1.3.8.1 (2013).

  • 89.

    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-2 (2013).

  • 90.

    Legendre, P. & Legendre, L. Numerical Ecology 2nd English edn (Elsevier, 1998).

  • 91.

    Montgomery, D. C. & Peck, E. A. Introduction to Linear Regression Analysis 2nd edn (John Wiley & Sons, 1992).

  • 92.

    Liu, Q. Variation partitioning by partial redundancy analysis (RDA). Environmetrics 8, 75–85 (1997).

    CAS  Article  Google Scholar 

  • 93.

    Kemppainen, P. et al. Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure. Mol. Ecol. Resour. 15, 1031–1045 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 94.

    Ohta, T. Linkage disequilibrium with the island model. Genetics 101, 139–155 (1982).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 95.

    Beissinger, T. M. et al. Using the variability of linkage disequilibrium between subpopulations to infer sweeps and epistatic selection in a diverse panel of chickens. Heredity 116, 58–166 (2015).

    Google Scholar 

  • 96.

    Hijmans, R. J. geosphere: Spherical trigonometry. R package version 1.5‐7 (2017).

  • 97.

    Adamack, A. T. & Gruber, B. PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).

    Article  Google Scholar 

  • 98.

    Gompert, Z. & Buerkle, A. C. introgress: methods for analyzing introgression between divergent lineages. R package version 1.2.3 (2012).

  • 99.

    Gompert, Z. & Buerkle, C. A. A powerful regression-based method for admixture mapping of isolation across the genome of hybrids. Mol. Ecol. 18, 1207–1224 (2009).

    PubMed  Article  PubMed Central  Google Scholar 

  • 100.

    Janoušek, V. et al. Genome‐wide architecture of reproductive isolation in a naturally occurring hybrid zone between Mus musculus musculus and M. m. domesticus. Mol. Ecol. 21, 3032–3047 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  • 101.

    Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome. Science 334, 83–86 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 102.

    Menon, M. et al. Data from: adaptive evolution in a confier hybrid zone is driven by a mosaic of recently introgressed and background genetic variants. Figshare, Dataset https://doi.org/10.6084/m9.figshare.c.5130104 (2020).

  • 103.

    Shirk, A. J. et al. Southwestern white pine (Pinus strobiformis) species distribution models predict large range shift and contraction due to climate change. For. Ecol. Manag. 411, 176–186 (2018).

    Article  Google Scholar 

  • 104.

    Little, E. L., Jr. Atlas of United States Trees, Vol. 1., Conifers and important hardwoods. Misc. Publ. 1146, 320 (U.S. Department of Agriculture, Forest Service, 1971).

  • 105.

    Menon, M. et al. Code from: adaptive evolution in a confier hybrid zone is driven by a mosaic of recently introgressed and background genetic variants. Zenodo, Dataset https://doi.org/10.5281/zenodo.4054085 (2020).


  • Source: Ecology - nature.com

    New fiber optic temperature sensing approach to keep fusion power plants running

    Reducing inequality across the globe and on campus