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Annual Review of Ecology, Evolution, and Systematics

Volume 51, 2020

Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Abstract

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In the coming decades, global climate change is expected to induce shifts in the selective pressures that shape this adaptive variation. These changes in selective pressures will likely result in varying degrees of local climate maladaptation and spatial reshuffling of the underlying distributions of adaptive alleles. There is a growing interest in using population genomic data to help predict future disruptions to locally adaptive gene-environment associations. One motivation behind such work is to better understand how the effects of changing climate on populations’ short-term fitness could vary spatially across species ranges. Here we review the current use of genomic data to predict the disruption of local adaptation across current and future climates. After assessing goals and motivationsunderlying the approach, we review the main steps and associated statistical methods currently in use and explore our current understanding of the limits and future potential of using genomics to predict climate change (mal)adaptation.

Keyword(s): assisted migration, climate change, conservation, genetic offset, genomic vulnerability, local adaptation
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/content/journals/10.1146/annurev-ecolsys-020720-042553
2020-11-02
2024-04-16
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Literature Cited

  1. Aguirre-Liguori JA, Ramírez-Barahona S, Tiffin P, Eguiarte LE 2019. Climate change is predicted to disrupt patterns of local adaptation in wild and cultivated maize. Proc. R. Soc. B 286:20190486 http://doi.org/10.1098/rspb.2019.0486
    [Crossref] [Google Scholar]
  2. Aitken SN, Bemmels JB. 2016. Time to get moving: assisted gene flow of forest trees. Evol. Appl. 9:1271–90 https://doi.org/10.1111/eva.12293
    [Crossref] [Google Scholar]
  3. Aitken SN, Whitlock MC. 2013. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44:367–88 https://doi.org/10.1146/annurev-ecolsys-110512-135747
    [Crossref] [Google Scholar]
  4. Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S 2008. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1:195–111 https://doi.org/10.1111/j.1752-4571.2007.00013.x
    [Crossref] [Google Scholar]
  5. Alberto FJ, Aitken SN, Alía R, González-Martínez SC et al. 2013a. Potential for evolutionary responses to climate change—evidence from tree populations. Glob. Change Biol. 19:61645–61 https://doi.org/10.1111/gcb.12181
    [Crossref] [Google Scholar]
  6. Alberto FJ, Derory J, Boury C, Frigerio JM, Zimmermann NE, Kremer A 2013b. Imprints of natural selection along environmental gradients in phenology-related genes of Quercus petraea. . Genetics 195:2495–512 https://doi.org/10.1534/genetics.113.153783
    [Crossref] [Google Scholar]
  7. Anderson JT, Wadgymar SM. 2020. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23:1181–92 https://doi.org/10.1111/ele.13427
    [Crossref] [Google Scholar]
  8. Barrett RDH, Schluter D. 2008. Adaptation from standing genetic variation. Trends Ecol. Evol. 23:138–44 https://doi.org/10.1016/j.tree.2007.09.008
    [Crossref] [Google Scholar]
  9. Bay RA, Harrigan RJ, Underwood VL, Gibbs HL, Smith TB, Ruegg K 2018. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359:637183–86 https://doi.org/10.1126/science.aan4380
    [Crossref] [Google Scholar]
  10. Benito Garzón M, Robson TM, Hampe A 2019. ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytol 222:41757–65 https://doi.org/10.1111/nph.15716
    [Crossref] [Google Scholar]
  11. Benning JW, Moeller DA. 2019. Maladaptation beyond a geographic range limit driven by antagonistic and mutualistic biotic interactions across an abiotic gradient. Evolution 73:102044–59 https://doi.org/10.1111/evo.13836
    [Crossref] [Google Scholar]
  12. Berg JJ, Coop G. 2014. A population genetic signal of polygenic adaptation. PLOS Genet 10:8e1004412 https://doi.org/10.1371/journal.pgen.1004412
    [Crossref] [Google Scholar]
  13. Blanquart F, Kaltz O, Nuismer SL, Gandon S 2013. A practical guide to measuring local adaptation. Ecol. Lett. 16:91195–205 https://doi.org/10.1111/ele.12150
    [Crossref] [Google Scholar]
  14. Bleeker W, Hurka H. 2001. Introgressive hybridization in Rorippa (Brassicaceae): gene flow and its consequences in natural and anthropogenic habitats. Mol. Ecol. 10:82013–22 https://doi.org/10.1046/j.1365-294X.2001.01341.x
    [Crossref] [Google Scholar]
  15. Blois JL, Zarnetske PL, Fitzpatrick MC, Finnegan S 2013. Climate change and the past, present, and future of biotic interactions. Science 341:6145499–504 https://doi.org/10.1126/science.1237184
    [Crossref] [Google Scholar]
  16. Bradshaw WE, Holzapfel CM. 2006. Evolutionary response to rapid climate change. Science 312:57791477–78 https://doi.org/10.1126/science.1127000
    [Crossref] [Google Scholar]
  17. Brady SP, Bolnick DI, Angert AL, Gonzalez A et al. 2019a. Causes of maladaptation. Evol. Appl. 12:71229–42 https://doi.org/10.1111/eva.12844
    [Crossref] [Google Scholar]
  18. Brady SP, Bolnick DI, Barrett RDH, Chapman L et al. 2019b. Understanding maladaptation by uniting ecological and evolutionary perspectives. Am. Nat. 194:4495–515 https://doi.org/10.1086/705020
    [Crossref] [Google Scholar]
  19. Brondizio ES, Settele J, Díaz S, Ngo HT 2019. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Rep., IPBES Secr. Bonn, Ger: https://ipbes.net/global-assessment
  20. Browne L, Wright JW, Fitz-Gibbon S, Gugger PF, Sork VL 2019. Adaptational lag to temperature in valley oak (Quercus lobata) can be mitigated by genome-informed assisted gene flow. PNAS 116:5025179–85 https://doi.org/10.1073/pnas.1908771116
    [Crossref] [Google Scholar]
  21. Capblancq T, Luu K, Blum MGB, Bazin E 2018. Evaluation of redundancy analysis to identify signatures of local adaptation. Mol. Ecol. Resour. 18:61223–33 https://doi.org/10.1111/1755-0998.12906
    [Crossref] [Google Scholar]
  22. Capblancq T, Morin X, Gueguen M, Renaud J, Lobreaux S, Bazin E 2020. Climate-associated genetic variation in Fagus sylvatica and potential responses to climate change in the French Alps. J. Evol. Biol. https://doi.org/10.1111/jeb.13610
    [Crossref] [Google Scholar]
  23. Carvalho CS, Forester BR, Mitre SK, Alves R, Imperatriz-Fonseca VL et al. 2019. Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration. BioRxiv 2019.12.11.872747. https://doi.org/10.1101/2019.12.11.872747
    [Crossref]
  24. Chen I-C, Hill JK, Ohlemüller R, Roy DB, Thomas CD 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333:60451024–26 https://doi.org/10.1126/science.1206432
    [Crossref] [Google Scholar]
  25. Chevin LM, Lande R, Mace GM 2010. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLOS Biol 8:4e1000357 https://doi.org/10.1371/journal.pbio.1000357
    [Crossref] [Google Scholar]
  26. Chhatre VE, Fetter KC, Gougherty AV, Fitzpatrick MC, Soolanayakanahally RY et al. 2019. Climatic niche predicts the landscape structure of locally adaptive standing genetic variation. BioRxiv 817411. https://doi.org/10.1101/817411
    [Crossref]
  27. Curtin SJ, Tiffin P, Guhlin J, Trujillo D, Burghart L et al. 2017. Validating genome-wide association candidates controlling quantitative variation in nodulation. Plant Physiol 173:2921–31 https://doi.org/10.1104/pp.16.01923
    [Crossref] [Google Scholar]
  28. Davis MB, Shaw RG. 2001. Range shifts and adaptive responses to quaternary climate change. Science 292:5517673–79 https://doi.org/10.1126/science.292.5517.673
    [Crossref] [Google Scholar]
  29. De Kort H, Vandepitte K, Bruun HH, Closset-Kopp D, Honnay O, Mergeay J 2014. Landscape genomics and a common garden trial reveal adaptive differentiation to temperature across Europe in the tree species Alnus glutinosa. Mol. . Ecol 23:194709–21 https://doi.org/10.1111/mec.12813
    [Crossref] [Google Scholar]
  30. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL 2013. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:2327–45 https://doi.org/10.1534/genetics.112.143313
    [Crossref] [Google Scholar]
  31. De Villemereuil P, Gaggiotti OE, Mouterde M, Till-Bottraud I 2016. Common garden experiments in the genomic era: new perspectives and opportunities. Heredity 116:3249–54 https://doi.org/10.1038/hdy.2015.93
    [Crossref] [Google Scholar]
  32. Derry AM, Fraser DJ, Brady SP, Astorg L, Lawrence ER et al. 2019. Conservation through the lens of (mal)adaptation: concepts and meta-analysis. Evol. Appl. 12:71287–304 https://doi.org/10.1111/eva.12791
    [Crossref] [Google Scholar]
  33. Deutsch CA, Tewksbury JJ, Tigchelaar M, Battisti DS, Merrill SC et al. 2018. Increase in crop losses to insect pests in a warming climate. Science 361:6405916–19 https://doi.org/10.1126/science.aat3466
    [Crossref] [Google Scholar]
  34. Ellis N, Smith SJ, Pitcher CR 2012. Gradient forests: calculating importance gradients on physical predictors. Ecology 93:1156–68 https://doi.org/10.1890/11-0252.1
    [Crossref] [Google Scholar]
  35. Etterson JR, Franks SJ, Mazer SJ, Shaw RG, Soper Gorden NL et al. 2016. Project baseline: an unprecedented resource to study plant evolution across space and time. Am. J. Bot. 103:1164–73 https://doi.org/10.3732/ajb.1500313
    [Crossref] [Google Scholar]
  36. Exposito-Alonso M, Burbano HA, Bossdorf O, Nielsen R, Weigel D 2019. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573:7772126–29 https://doi.org/10.1038/s41586-019-1520-9
    [Crossref] [Google Scholar]
  37. Exposito-Alonso M, Vasseur F, Ding W, Wang G, Burbano HA, Weigel D 2018. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat. Ecol. Evol 2:2352–58 https://doi.org/10.1038/s41559-017-0423-0
    [Crossref] [Google Scholar]
  38. Ferrier S, Guisan A. 2006. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43:3393–404 https://doi.org/10.1111/j.1365-2664.2006.01149.x
    [Crossref] [Google Scholar]
  39. Fitzpatrick MC, Blois JL, Williams JW, Nieto-Lugilde D, Maguire KC, Lorenz DJ 2018a. How will climate novelty influence ecological forecasts? Using the quaternary to assess future reliability. Glob. Change Biol. 24:83575–86 https://doi.org/10.1111/gcb.14138
    [Crossref] [Google Scholar]
  40. Fitzpatrick MC, Keller SR. 2015. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18:11–16 https://doi.org/10.1111/ele.12376
    [Crossref] [Google Scholar]
  41. Fitzpatrick MC, Keller SR, Lotterhos KE 2018b. Comment on ‘Genomic signals of selection predict climate-driven population declines in a migratory bird. .’ Science 361:64012–4
     [Google Scholar]
  42. Forester BR, Dechaine EG, Bunn AG 2013. Integrating ensemble species distribution modelling and statistical phylogeography to inform projections of climate change impacts on species distributions. Divers. Distrib. 19:121480–95 https://doi.org/10.1111/ddi.12098
    [Crossref] [Google Scholar]
  43. Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR 2016. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25:1104–20 https://doi.org/10.1111/mec.13476
    [Crossref] [Google Scholar]
  44. Forester BR, Lasky JR, Wagner HH, Urban DL 2018. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27:92215–33 https://doi.org/10.1111/mec.14584
    [Crossref] [Google Scholar]
  45. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM 2011. A map of local adaptation in Arabidopsis thaliana. . Science 334:605286–89 https://doi.org/10.1126/science.1209271
    [Crossref] [Google Scholar]
  46. François O, Martins H, Caye K, Schoville S 2016. Controlling false discoveries in genome scans for selection. Mol. Ecol. 25:454–69 https://doi.org/10.1111/mec.13513
    [Crossref] [Google Scholar]
  47. Franks SJ, Hamann E, Weis AE 2018. Using the resurrection approach to understand contemporary evolution in changing environments. Evol. Appl. 11:117–28 https://doi.org/10.1111/eva.12528
    [Crossref] [Google Scholar]
  48. Franks SJ, Hoffmann AA. 2012. Genetics of climate change adaptation. Annu. Rev. Genet. 46:185–208 https://doi.org/10.1146/annurev-genet-110711-155511
    [Crossref] [Google Scholar]
  49. Gienapp P, Calus MPL, Laine VN, Visser ME 2019. Genomic selection on breeding time in a wild bird population. Evol. Lett. 3:2142–51 https://doi.org/10.1002/evl3.103
    [Crossref] [Google Scholar]
  50. Gougherty AV, Keller SR, Chhatre VE, Fitzpatrick MC 2020. Future climate change promotes novel gene-climate associations in balsam poplar 2 (Populus balsamifera L.), a forest tree species. BioRxiv 2020.02.28.961060. https://doi.org/10.1101/2020.02.28.961060
    [Crossref]
  51. Grant PR, Grant BR. 2016. Introgressive hybridization and natural selection in Darwin's finches. Biol. J. Linn. Soc. 117:4812–22 https://doi.org/10.1111/bij.12702
    [Crossref] [Google Scholar]
  52. Grant PR, Grant BR, Markert JA, Keller LF, Petren K 2004. Convergent evolution of Darwin's finches caused by introgressive hybridization and selection. Evolution 58:71588–99 https://doi.org/10.1554/04-016
    [Crossref] [Google Scholar]
  53. Greenham K, McClung CR. 2015. Integrating circadian dynamics with physiological processes in plants. Nat. Rev. Genet. 16:10598–610 https://doi.org/10.1038/nrg3976
    [Crossref] [Google Scholar]
  54. Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8:9993–1009 https://doi.org/10.1111/j.1461-0248.2005.00792.x
    [Crossref] [Google Scholar]
  55. Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB et al. 2011. Arabidopsis thaliana genome. Science 334:605283–86 https://doi.org/10.1126/science.1209244
    [Crossref] [Google Scholar]
  56. Harbicht A, Wilson CC, Fraser DJ 2014. Does human-induced hybridization have long-term genetic effects? Empirical testing with domesticated, wild and hybridized fish populations. Evol. Appl. 7:101180–91 https://doi.org/10.1111/eva.12199
    [Crossref] [Google Scholar]
  57. Hereford J. 2009. A quantitative survey of local adaptation and fitness trade‐offs. Am. Nat. 173:5579–88 https://doi.org/10.1086/597611
    [Crossref] [Google Scholar]
  58. Hoban S, Kelley JL, Lotterhos KE, Antolin MF, Bradburd G et al. 2016. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am. Nat. 188:4379–97 https://doi.org/10.1086/688018
    [Crossref] [Google Scholar]
  59. Hoffmann AA, Sgró CM. 2011. Climate change and evolutionary adaptation. Nature 470:7335479–85 https://doi.org/10.1038/nature09670
    [Crossref] [Google Scholar]
  60. Hohenlohe PA, Amish SJ, Catchen JM, Allendorf FW, Luikart G 2011. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol. Ecol. Resour. 11:Suppl. 1117–22 https://doi.org/10.1111/j.1755-0998.2010.02967.x
    [Crossref] [Google Scholar]
  61. Info Flora. 2020. Arabis Alpina L subsp. Alpine. Subspecies 39400 Info Flora, Geneva: https://www.infoflora.ch/en/flora/arabis-alpina-subsp-alpina.html
    [Google Scholar]
  62. Ingvarsson PK, Bernhardsson C. 2018. Genome‐wide signatures of environmental adaptation in European aspen (Populus tremula) under current and future climate conditions. Evol. Appl. 13:1132–42 https://doi.org/10.1111/eva.12792
    [Crossref] [Google Scholar]
  63. Ioannidis JPA, Thomas G, Daly MJ 2009. Validating, augmenting and refining genome-wide association signals. Nat. Rev. Genet. 10:5318–29 https://doi.org/10.1038/nrg2544
    [Crossref] [Google Scholar]
  64. Jaramillo-Correa JP, Rodríguez-Quilón I, Grivet D, Lepoittevin C, Sebastiani F et al. 2015. Molecular proxies for climate maladaptation in a long-lived tree (Pinus pinaster Aiton, Pinaceae). Genetics 199:3793–807 https://doi.org/10.1534/genetics.114.173252
    [Crossref] [Google Scholar]
  65. Jay F, Manel S, Alvarez N, Durand EY, Thuiller W et al. 2012. Forecasting changes in population genetic structure of alpine plants in response to global warming. Mol. Ecol. 21:102354–68 https://doi.org/10.1111/j.1365-294X.2012.05541.x
    [Crossref] [Google Scholar]
  66. Jones FC, Grabherr MG, Chan YF, Russell P, Mauceli E et al. 2012. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484:739255–61 https://doi.org/10.1038/nature10944
    [Crossref] [Google Scholar]
  67. Jones MR, Mills LS, Alves PC, Callahan CM, Alves JM et al. 2018. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares. Science 1358:63951355–58
     [Google Scholar]
  68. Josephs EB, Berg JJ, Ross-Ibarra J, Coop G 2019. Detecting adaptive differentiation in structured populations with genomic data and common gardens. Genetics 211:3989–1004 https://doi.org/10.1534/genetics.118.301786
    [Crossref] [Google Scholar]
  69. Jump AS, Hunt JM, Martínez-Izquierdo JA, Peñuelas J 2006. Natural selection and climate change: temperature-linked spatial and temporal trends in gene frequency in Fagus sylvatica. Mol. Ecol 15:113469–80 https://doi.org/10.1111/j.1365-294X.2006.03027.x
    [Crossref] [Google Scholar]
  70. Jump AS, Peñuelas J. 2005. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8:91010–20 https://doi.org/10.1111/j.1461-0248.2005.00796.x
    [Crossref] [Google Scholar]
  71. Kawecki TJ, Ebert D. 2004. Conceptual issues in local adaptation. Ecol. Lett. 7:121225–41 https://doi.org/10.1111/j.1461-0248.2004.00684.x
    [Crossref] [Google Scholar]
  72. Keller SR, Chhatre VE, Fitzpatrick MC 2018. Influence of range position on locally adaptive gene-environment associations in Populus flowering time genes. J. Hered. 109:147–58 https://doi.org/10.1093/jhered/esx098
    [Crossref] [Google Scholar]
  73. Lang PLM, Willems FM, Scheepens JF, Burbano HA, Bossdorf O 2019. Using herbaria to study global environmental change. New Phytol 221:1110–22 https://doi.org/10.1111/nph.15401
    [Crossref] [Google Scholar]
  74. Lasky JR, Forester BR, Reimherr M 2018. Coherent synthesis of genomic associations with phenotypes and home environments. Mol. Ecol. Resour. 18:191–106 https://doi.org/10.1111/1755-0998.12714
    [Crossref] [Google Scholar]
  75. Leimu R, Fischer M. 2008. A meta-analysis of local adaptation in plants. PLOS ONE 3:12e4010 https://doi.org/10.1371/journal.pone.0004010
    [Crossref] [Google Scholar]
  76. Lenormand T. 2002. Gene flow and the limits to natural selection. Trends Ecol. Evol. 17:4183–89 https://doi.org/10.1016/S0169-5347(02)02497-7
    [Crossref] [Google Scholar]
  77. Lotterhos KE, Whitlock MC. 2014. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol. Ecol. 23:92178–92 https://doi.org/10.1111/mec.12725
    [Crossref] [Google Scholar]
  78. Lotterhos KE, Whitlock MC. 2015. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24:1031–46 https://doi.org/10.1111/mec.13100
    [Crossref] [Google Scholar]
  79. Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK et al. 2017. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol. Ecol. Resour. 17:2142–52 https://doi.org/10.1111/1755-0998.12635
    [Crossref] [Google Scholar]
  80. Mahony CR, MacLachlan IR, Lind BM, Yoder JB, Wang T, Aitken SN 2020. Evaluating genomic data for management of local adaptation in a changing climate: a lodgepole pine case study. Evol. Appl. 13:1116–31 https://doi.org/10.1111/eva.12871
    [Crossref] [Google Scholar]
  81. Manel S, Schwartz MK, Luikart G, Taberlet P 2003. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18:4189–97 https://doi.org/10.1016/S0169-5347(03)00008-9
    [Crossref] [Google Scholar]
  82. Martins K, Gugger PF, Llanderal-Mendoza J, González-Rodríguez A, Fitz-Gibbon ST et al. 2018. Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa. Evol. . Appl 11:101842–58 https://doi.org/10.1111/eva.12684
    [Crossref] [Google Scholar]
  83. Monroe JG, Powell T, Price N, Mullen JL, Howard A et al. 2018. Drought adaptation in Arabidopsis thaliana by extensive genetic loss-of-function. eLife 7:e41038 https://doi.org/10.7554/eLife.41038
    [Crossref] [Google Scholar]
  84. Morton EM, Rafferty NE. 2017. Plant-pollinator interactions under climate change: the use of spatial and temporal transplants. Appl. Plant Sci. 5:61600133 https://doi.org/10.3732/apps.1600133
    [Crossref] [Google Scholar]
  85. Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C 2005. Genomic scans for selective sweeps using SNP data. Genome Res 15:111566–75 https://doi.org/10.1101/gr.4252305
    [Crossref] [Google Scholar]
  86. Oetting WS, Jacobson PA, Israni AK 2018. Validation is critical for genome-wide association study–based associations. Am. J. Transplant. 17:2318–19 https://doi.org/10.1111/ajt.14051
    [Crossref] [Google Scholar]
  87. Olson MS, Levsen N, Soolanayakanahally RY, Guy RD, Schroeder WR et al. 2013. The adaptive potential of Populus balsamifera L. to phenology requirements in a warmer global climate. Mol. Ecol. 22:51214–30 https://doi.org/10.1111/mec.12067
    [Crossref] [Google Scholar]
  88. Palumbi SR, Barshis DJ, Traylor-Knowles N, Bay RA 2014. Mechanisms of reef coral resistance to future climate change. Science 344:6186895–97 https://science.sciencemag.org/content/344/6186/895.long
    [Google Scholar]
  89. Parmesan C, Yohe G. 2003. A globally coherent fingerprint of climate change. Nature 421:37–42 https://doi.org/10.1038/nature01286
    [Crossref] [Google Scholar]
  90. Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge, UK: Cambridge Univ. Press
  91. Pina-Martins F, Baptista J, Pappas G, Paulo OS 2019. New insights into adaptation and population structure of cork oak using genotyping by sequencing. Glob. Change Biol. 25:1337–50 https://doi.org/10.1111/gcb.14497
    [Crossref] [Google Scholar]
  92. Price TD, Qvarnström A, Irwin DE 2003. The role of phenotypic plasticity in driving genetic evolution. Proc. R. Soc. B 270:15231433–40 https://doi.org/10.1098/rspb.2003.2372
    [Crossref] [Google Scholar]
  93. Prunier J, Laroche J, Beaulieu J, Bousquet J 2011. Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce. Mol. Ecol. 20:81702–16 https://doi.org/10.1111/j.1365-294X.2011.05045.x
    [Crossref] [Google Scholar]
  94. Razgour O, Forester B, Taggart JB, Bekaert M, Juste J et al. 2019. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. PNAS 116:2110418–23 https://doi.org/10.1073/pnas.1820663116
    [Crossref] [Google Scholar]
  95. Rehfeldt GE, Tchebakova NM, Parfenova YI, Wykoff WR, Kuzmina NA, Milyutin LI 2002. Intraspecific responses to climate in Pinus sylvestris. Glob. . Change Biol 8:9912–29 https://doi.org/10.1046/j.1365-2486.2002.00516.x
    [Crossref] [Google Scholar]
  96. Reich PB, Sendall KM, Stefanski A, Rich RL, Hobbie SE, Montgomery RA 2018. Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture. Nature 562:7726263–67 https://doi.org/10.1038/s41586-018-0582-4
    [Crossref] [Google Scholar]
  97. Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R 2015. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24:174348–70 https://doi.org/10.1111/mec.13322
    [Crossref] [Google Scholar]
  98. Rellstab C, Zoller S, Walthert L, Lesur I, Pluess AR et al. 2016. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25:235907–24 https://doi.org/10.1111/mec.13889
    [Crossref] [Google Scholar]
  99. Resende FR Jr., Muñoz P, Resende MDV, Garrick DJ, Fernando RL et al. 2012. Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.). Genetics 190:41503–10 https://doi.org/10.1534/genetics.111.137026
    [Crossref] [Google Scholar]
  100. Rochat E, Joost S. 2019. Spatial areas of genotype probability (SPAG): predicting the spatial distribution of adaptive genetic variants under future climatic conditions. BioRxiv 2019.12.20.884114. https://doi.org/10.1101/2019.12.20.884114
    [Crossref]
  101. Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V et al. 2018. Functional validation of candidate genes detected by genomic feature models. G3 8:51659–68 https://doi.org/10.1534/g3.118.200082
    [Crossref] [Google Scholar]
  102. Ruegg K, Bay RA, Anderson EC, Saracco JF, Harrigan RJ et al. 2018. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21:71085–96 https://doi.org/10.1111/ele.12977
    [Crossref] [Google Scholar]
  103. Sabeti PC, Reich DE, Higgins JM, Levine HZP, Richter DJ et al. 2002. Detecting recent positive selection in the human genome from haplotype structure. Nature 419:6909832–37 https://doi.org/10.1038/nature01140
    [Crossref] [Google Scholar]
  104. Saino N, Bazzi G, Gatti E, Caprioli M, Cecere JG et al. 2015. Polymorphism at the clock gene predicts phenology of long-distance migration in birds. Mol. Ecol. 24:81758–73 https://doi.org/10.1111/mec.13159
    [Crossref] [Google Scholar]
  105. Santure AW, Garant D 2018. Wild GWAS—association mapping in natural populations. Mol. Ecol. Resour. 18:4):, 729–38.. https://doi.org/10.1111/1755-0998.12901
    [Crossref] [Google Scholar]
  106. Savolainen O, Lascoux M, Merilä J 2013. Ecological genomics of local adaptation. Nat. Rev. Genet. 14:11807–20 https://doi.org/10.1038/nrg3522
    [Crossref] [Google Scholar]
  107. Savolainen O, Pyhäjärvi T. 2007. Genomic diversity in forest trees. Curr. Opin. Plant Biol. 10:2162–67 https://doi.org/10.1016/j.pbi.2007.01.011
    [Crossref] [Google Scholar]
  108. Seehausen O, van Alphen JJM, Witte F 1997. Cichlid fish diversity threatened by eutrophication that curbs sexual selection. Science 277:53331808–11 https://doi.org/10.1126/science.277.5333.1808
    [Crossref] [Google Scholar]
  109. Shaw RG. 2019. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193:11–10 https://doi.org/10.1086/700565
    [Crossref] [Google Scholar]
  110. Somero GN. 2010. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers. .’ J. Exp. Biol. 213:6912–20 https://doi.org/10.1242/jeb.037473
    [Crossref] [Google Scholar]
  111. Sork VL, Squire K, Gugger PF, Steele SE, Levy ED, Eckert AJ 2016. Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak. Quercus lobata. Am. J. Bot. 103:133–46 https://doi.org/10.3732/ajb.1500162
    [Crossref] [Google Scholar]
  112. Steane DA, Potts BM, McLean E, Prober SM, Stock WD et al. 2014. Genome-wide scans detect adaptation to aridity in a widespread forest tree species. Mol. Ecol. 23:102500–13 https://doi.org/10.1111/mec.12751
    [Crossref] [Google Scholar]
  113. Stillman JH. 2003. Acclimation capacity underlies susceptibility to climate change. Science 301:562965 https://doi.org/10.1126/science.1083073
    [Crossref] [Google Scholar]
  114. Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK et al. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge, UK: Cambridge Univ. Press
  115. Supple MA, Bragg JG, Broadhurst LM, Nicotra AB, Byrne M et al. 2018. Landscape genomic prediction for restoration of a eucalyptus foundation species under climate change. eLife 7:31835 https://doi.org/10.7554/eLife.31835
    [Crossref] [Google Scholar]
  116. Svenning JC, Skov F. 2007. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation. ? Ecol. Lett. 10:6453–60 https://doi.org/10.1111/j.1461-0248.2007.01038.x
    [Crossref] [Google Scholar]
  117. Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M et al. 2008. Predicting global change impacts on plant species’ distributions: future challenges. Perspect. Plant Ecol. Evol. Syst. 9:3–4137–52 https://doi.org/10.1016/j.ppees.2007.09.004
    [Crossref] [Google Scholar]
  118. Tiffin P, Ross-Ibarra J. 2014. Advances and limits of using population genetics to understand local adaptation. Trends Ecol. Evol. 29:12673–80 https://doi.org/10.1016/j.tree.2014.10.004
    [Crossref] [Google Scholar]
  119. Tigano A, Friesen VL. 2016. Genomics of local adaptation with gene flow. Mol. Ecol. 25:102144–64 https://doi.org/10.1111/mec.13606
    [Crossref] [Google Scholar]
  120. Veloz SD, Williams JW, Blois JL, He F, Otto-Bliesner B, Liu Z 2012. No-analog climates and shifting realized niches during the late quaternary: implications for 21st-century predictions by species distribution models. Glob. Change Biol. 18:51698–713 https://doi.org/10.1111/j.1365-2486.2011.02635.x
    [Crossref] [Google Scholar]
  121. Vitti JJ, Grossman SR, Sabeti PC 2013. Detecting natural selection in genomic data. Annu. Rev. Genet. 47:97–120 https://doi.org/10.1146/annurev-genet-111212-133526
    [Crossref] [Google Scholar]
  122. Williams JW, Jackson ST. 2007. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5:9475–82 https://doi.org/10.1890/070037
    [Crossref] [Google Scholar]
  123. Williams JW, Jackson ST, Kutzbach JE 2007. Projected distributions of novel and disappearing climates by 2100 AD. PNAS 104:145738–42 https://doi.org/10.1073/pnas.0606292104
    [Crossref] [Google Scholar]
  124. Yoder JB, Stanton-Geddes J, Zhou P, Briskine R, Young ND, Tiffin P 2014. Genomic signature of adaptation to climate in Medicago truncatula. . Genetics 196:41263–75 https://doi.org/10.1534/genetics.113.159319
    [Crossref] [Google Scholar]
  125. Zhou X, Stephens M. 2014. Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat. Methods 11:4407–9 https://doi.org/10.1038/nmeth.2848
    [Crossref] [Google Scholar]
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Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes
Annual Review of Ecology, Evolution, and Systematics 51, 245 (2020); https://doi.org/10.1146/annurev-ecolsys-020720-042553
/content/journals/10.1146/annurev-ecolsys-020720-042553
/content/journals/10.1146/annurev-ecolsys-020720-042553
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