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Genomics for monitoring and understanding species responses to global climate change

Nature Reviews Genetics volume 25, pages 165–183 (2024)Cite this article

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Abstract

All life forms across the globe are experiencing drastic changes in environmental conditions as a result of global climate change. These environmental changes are happening rapidly, incur substantial socioeconomic costs, pose threats to biodiversity and diminish a species’ potential to adapt to future environments. Understanding and monitoring how organisms respond to human-driven climate change is therefore a major priority for the conservation of biodiversity in a rapidly changing environment. Recent developments in genomic, transcriptomic and epigenomic technologies are enabling unprecedented insights into the evolutionary processes and molecular bases of adaptation. This Review summarizes methods that apply and integrate omics tools to experimentally investigate, monitor and predict how species and communities in the wild cope with global climate change, which is by genetically adapting to new environmental conditions, through range shifts or through phenotypic plasticity. We identify advantages and limitations of each method and discuss future research avenues that would improve our understanding of species’ evolutionary responses to global climate change, highlighting the need for holistic, multi-omics approaches to ecosystem monitoring during global climate change.

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Fig. 1: A conceptual framework to assess the effects of global climate change using omics approaches.
Fig. 2: Using genotype–environment associations to identify candidate SNPs and potential sources of adaptive variation.
Fig. 3: Integrated approach to synthesize levels of risk of teosinte to GCC, based on ecological niche models, landscape resistance, local agentic adaptation, genomic offset, gene flow and genomic load.
Fig. 4: Experimental evolution: evolve and resequence.
Fig. 5: Combining resurrection ecology and genomics.
Fig. 6: The role of transcriptional and epigenetic plasticity in response to global climate change.
Fig. 7: eDNA metabarcoding approaches to monitor temporal switches in community composition with global climate change.

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References

  1. Smith, T. B. & Bernatchez, L. Evolutionary change in human-altered environments. Mol. Ecol. 17, 1–8 (2008).

    PubMed  Google Scholar 

  2. Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    PubMed  PubMed Central  Google Scholar 

  3. Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).

    PubMed  Google Scholar 

  5. Waldvogel, A.-M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18 (2020). A road map of how science and society can work together to facilitate sampling, estimating of fitness parameters and genome sequencing for a broad range of species to implement mitigation measures to face GCC.

    PubMed  PubMed Central  Google Scholar 

  6. Lancaster, L. T. et al. Understanding climate change response in the age of genomics. J. Anim. Ecol. 91, 1056–1063 (2022). Special issue highlighting how emerging genomic approaches are used to understand population responses to GCC across a diverse range of animal systems.

    PubMed  Google Scholar 

  7. Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Phil. Trans. R. Soc. B 367, 1665–1679 (2012).

    PubMed  PubMed Central  Google Scholar 

  8. Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Glob. Change Biol. 28, 1753–1765 (2022).

    CAS  Google Scholar 

  9. Sandoval-Castillo, J. et al. Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions. Proc. Natl Acad. Sci. USA 117, 17112–17121 (2020).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  10. Eisenhauer, N. et al. The dark side of animal phenology. Trends Ecol. Evol. 33, 898–901 (2018).

    PubMed  Google Scholar 

  11. Miller-Rushing, A. J. & Primack, R. B. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89, 332–341 (2008).

    PubMed  Google Scholar 

  12. Bruno, J. F. et al. Thermal stress and coral cover as drivers of coral disease outbreaks. PLOS Biol. 5, e124 (2007).

    PubMed  PubMed Central  Google Scholar 

  13. Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).

    PubMed  Google Scholar 

  14. Parmesan, C. & Singer, M. C. Mosaics of climatic stress across species’ ranges: tradeoffs cause adaptive evolution to limits of climatic tolerance. Phil. Trans. R. Soc. B 377, 20210003 (2022).

    PubMed  PubMed Central  Google Scholar 

  15. Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).

    CAS  PubMed  ADS  Google Scholar 

  16. Carroll, S. P. et al. Applying evolutionary biology to address global challenges. Science 346, 1245993 (2014).

    PubMed  PubMed Central  Google Scholar 

  17. Donelson, J. M. et al. Understanding interactions between plasticity, adaptation and range shifts in response to marine environmental change. Phil. Trans. R. Soc. B 374, 20180186 (2019).

    PubMed  PubMed Central  Google Scholar 

  18. Hansen, M. M., Olivieri, I., Waller, D. M., Nielsen, E. E. & Group, T. G. W. Monitoring adaptive genetic responses to environmental change. Mol. Ecol. 21, 1311–1329 (2012).

    PubMed  Google Scholar 

  19. Verhoeven, K. J. F., vonHoldt, B. M. & Sork, V. L. Epigenetics in ecology and evolution: what we know and what we need to know. Mol. Ecol. 25, 1631–1638 (2016).

    PubMed  Google Scholar 

  20. Everett, L. J. et al. Gene expression networks in the Drosophila genetic reference panel. Genome Res. 30, 485–496 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Yu, Y. & Bergland, A. O. Distinct signals of clinal and seasonal allele frequency change at eQTLs in Drosophila melanogaster. Evol 76, 2758–2768 (2022).

    CAS  Google Scholar 

  22. Stange, M., Barrett, R. D. H. & Hendry, A. P. The importance of genomic variation for biodiversity, ecosystems and people. Nat. Rev. Genet. 22, 89–105 (2021).

    CAS  PubMed  Google Scholar 

  23. McGaughran, A., Laver, R. & Fraser, C. Evolutionary responses to warming. Trends Ecol. Evol. 36, 591–600 (2021).

    PubMed  Google Scholar 

  24. Springer, N. M. & Schmitz, R. J. Exploiting induced and natural epigenetic variation for crop improvement. Nat. Rev. Genet. 18, 563–575 (2017).

    CAS  PubMed  Google Scholar 

  25. Logsdon, G. A., Vollger, M. R. & Eichler, E. E. Long-read human genome sequencing and its applications. Nat. Rev. Genet. 21, 597–614 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. De Coster, W., Weissensteiner, M. H. & Sedlazeck, F. J. Towards population-scale long-read sequencing. Nat. Rev. Genet. 22, 572–587 (2021).

    PubMed  PubMed Central  Google Scholar 

  27. Thomas, L. et al. Spatially varying selection between habitats drives physiological shifts and local adaptation in a broadcast spawning coral on a remote atoll in Western Australia. Sci. Adv. 8, eabl9185 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Hoban, S. et al. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am. Nat. 188, 379–397 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. Boulanger, E. et al. Climate differently influences the genomic patterns of two sympatric marine fish species. J. Anim. Ecol. 91, 1180–1195 (2022).

    PubMed  Google Scholar 

  30. Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M. & Holderegger, R. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).

    PubMed  Google Scholar 

  31. Lasky, J. R., Josephs, E. B. & Morris, G. P. Genotype–environment associations to reveal the molecular basis of environmental adaptation. Plant Cell 35, 125–138 (2023).

    PubMed  Google Scholar 

  32. Alvarado, A. H. et al. Genotype–environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence. Evol. Appl. 15, 1390–1407 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Nielsen, E. S., Henriques, R., Beger, M., Toonen, R. J. & Von der Heyden, S. Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species. BMC Evol. Biol. 20, 1–17 (2020).

    Google Scholar 

  34. Brauer, C. J., Unmack, P. J., Smith, S., Bernatchez, L. & Beheregaray, L. B. On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol. Ecol. 27, 3484–3497 (2018).

    CAS  PubMed  Google Scholar 

  35. Grummer, J. A. et al. Aquatic landscape genomics and environmental effects on genetic variation. Trends Ecol. Evol. 34, 641–654 (2019).

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  37. Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol. Ecol. 27, 2215–2233 (2018).

    CAS  PubMed  Google Scholar 

  38. Capblancq, T., Luu, K., Blum, M. G. & Bazin, E. Evaluation of redundancy analysis to identify signatures of local adaptation. Mol. Ecol. Resour. 18, 1223–1233 (2018).

    CAS  PubMed  Google Scholar 

  39. Martínez-Berdeja, A. et al. Functional variants of DOG1 control seed chilling responses and variation in seasonal life-history strategies in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 117, 2526–2534 (2020).

    PubMed  PubMed Central  ADS  Google Scholar 

  40. Fournier‐Level, A. et al. Adaptive significance of flowering time variation across natural seasonal environments in Arabidopsis thaliana. N. Phytol. 234, 719–734 (2022).

    Google Scholar 

  41. Capblancq, T. & Forester, B. R. Redundancy analysis: a Swiss Army knife for landscape genomics. Methods Ecol. Evol. 12, 2298–2309 (2021). A review of the application and challenges of RDA for understanding the relationship between genetic variation and the environment, with a case study and associated tutorial for users.

    Google Scholar 

  42. Booker, T. R., Yeaman, S., Whiting, J. R. & Whitlock, M. C. The WZA: a window-based method for characterizing genotype-environment associations. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13768 (2023).

  43. Meek, M. H. et al. Understanding local adaptation to prepare populations for climate change. Bioscience 73, 36–47 (2023).

    Google Scholar 

  44. Beer, M. A., Kane, R. A., Micheletti, S. J., Kozakiewicz, C. P. & Storfer, A. Landscape genomics of the streamside salamander: implications for species management in the face of environmental change. Evol. Appl. 15, 220–236 (2022). A demonstration of the use of GEAs to detect adaptive variation for understanding the potential for adaptation to environmental challenges across a heterogeneous landscape.

    PubMed  PubMed Central  Google Scholar 

  45. Aguirre‐Liguori, J. et al. Connecting genomic patterns of local adaptation and niche suitability in teosintes. Mol. Ecol. 26, 4226–4240 (2017).

    PubMed  Google Scholar 

  46. Flanagan, S. P., Forester, B. R., Latch, E. K., Aitken, S. N. & Hoban, S. Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol. Appl. 11, 1035–1052 (2018).

    PubMed  Google Scholar 

  47. Xuereb, A., d’Aloia, C. C., Andrello, M., Bernatchez, L. & Fortin, M. J. Incorporating putatively neutral and adaptive genomic data into marine conservation planning. Conserv. Biol. 35, 909–920 (2020).

    PubMed  Google Scholar 

  48. Forester, B. R. et al. Genomics‐informed delineation of conservation units in a desert amphibian. Mol. Ecol. 31, 5249–5269 (2022).

    PubMed  PubMed Central  Google Scholar 

  49. Mahony, C. R. et al. Evaluating genomic data for management of local adaptation in a changing climate: a lodgepole pine case study. Evol. Appl. 13, 116–131 (2020).

    PubMed  Google Scholar 

  50. Chen, Z. et al. Applying genomics in assisted migration under climate change: framework, empirical applications, and case studies. Evol. Appl. 15, 3–21 (2021).

    MathSciNet  PubMed  PubMed Central  Google Scholar 

  51. Lotterhos, K. E. The paradox of adaptive trait clines with nonclinal patterns in the underlying genes. Proc. Natl Acad. Sci. USA 120, e2220313120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Rockman, M. V. THE QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).

    PubMed  Google Scholar 

  53. Rougemont, Q. et al. Long-distance migration is a major factor driving local adaptation at continental scale in Coho salmon. Mol. Ecol. 32, 542–559 (2023).

    CAS  PubMed  Google Scholar 

  54. Rybnikov, S. R., Frenkel, Z., Hübner, S., Weissman, D. B. & Korol, A. B. Modeling the evolution of recombination plasticity: a prospective review. BioEssays 45, e2200237 (2023).

    PubMed  Google Scholar 

  55. Schlötterer, C. How predictable is adaptation from standing genetic variation? Experimental evolution in Drosophila highlights the central role of redundancy and linkage disequilibrium. Phil. Trans. R. Soc. B 378, 20220046 (2023).

    PubMed  PubMed Central  Google Scholar 

  56. Exposito-Alonso, M., Burbano, H. A., Bossdorf, O., Nielsen, R. & Weigel, D. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573, 126–129 (2019). A large-scale common garden experiment showing differences in relative fitness under climate change associated with candidate genomic regions in Arabidopsis thaliana.

    CAS  PubMed  ADS  Google Scholar 

  57. Mitchell-Olds, T. & Schmitt, J. Genetic mechanisms and evolutionary significance of natural variation in Arabidopsis. Nature 441, 947–952 (2006).

    CAS  PubMed  ADS  Google Scholar 

  58. Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015). Adapted the GF approach on SNP data set, originally developed to model spatial variation in community composition, to model turnover in allele frequency and called it ‘genetic offset’.

    PubMed  Google Scholar 

  59. Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018). Among the first studies to assess genomic offset on natural populations and link it with observed losses in fitness (population declines) in order to provide a rigorous validation of the predictions.

    CAS  PubMed  ADS  Google Scholar 

  60. Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020). Review of the main steps and associated statistical methods in genomic prediction of maladaptation across current and future climatic landscapes.

    Google Scholar 

  61. Rellstab, C., Dauphin, B. & Exposito-Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).

    PubMed  PubMed Central  Google Scholar 

  62. Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).

    PubMed  Google Scholar 

  63. Ingvarsson, P. K. & Bernhardsson, C. Genome-wide signatures of environmental adaptation in European aspen (Populus tremula) under current and future climate conditions. Evol. Appl. 13, 132–142 (2019).

    PubMed  PubMed Central  Google Scholar 

  64. Martins, K. et al. Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa. Evol. Appl. 11, 1842–1858 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).

    PubMed  Google Scholar 

  66. Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).

    Google Scholar 

  67. Supple, M. A. et al. Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change. eLife 7, e31835 (2018).

    PubMed  PubMed Central  Google Scholar 

  68. Steane, D. A. et al. Genome-wide scans detect adaptation to aridity in a widespread forest tree species. Mol. Ecol. 23, 2500–2513 (2014).

    PubMed  Google Scholar 

  69. Carvalho, C. S. et al. Combining genotype, phenotype, and environmental data to delineate site-adjusted provenance strategies for ecological restoration. Mol. Ecol. Resour. 21, 44–58 (2021).

    PubMed  Google Scholar 

  70. Rellstab, C. et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25, 5907–5924 (2016).

    PubMed  Google Scholar 

  71. Pina-Martins, F., Baptista, J., Pappas, G. Jr & Paulo, O. S. New insights into adaptation and population structure of cork oak using genotyping by sequencing. Glob. Change Biol. 25, 337–350 (2019).

    ADS  Google Scholar 

  72. Rochat, E., Selmoni, O. & Joost, S. Spatial areas of genotype probability: predicting the spatial distribution of adaptive genetic variants under future climatic conditions. Divers. Distrib. 27, 1076–1090 (2021).

    Google Scholar 

  73. Gain, C. et al. A quantitative theory for genomic offset statistics. Mol. Biol. Evol. 40, 6 (2023).

    Google Scholar 

  74. Hoffmann, A. A., Weeks, A. R. & Sgrò, C. M. Opportunities and challenges in assessing climate change vulnerability through genomics. Cell 184, 1420–1425 (2021). Describes the limitations and their respective solutions in genomic vulnerability assessments.

    CAS  PubMed  Google Scholar 

  75. Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021).

    PubMed  Google Scholar 

  76. Aguirre‐Liguori, J. A. et al. Divergence with gene flow is driven by local adaptation to temperature and soil phosphorus concentration in teosinte subspecies (Zea mays parviglumis and Zea mays mexicana). Mol. Ecol. 28, 2814–2830 (2019).

    PubMed  Google Scholar 

  77. Brauer, C. J. et al. Natural hybridization reduces vulnerability to climate change. Nat. Clim. Change 13, 282–289 (2023).

    ADS  Google Scholar 

  78. Rhoné, B. et al. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat. Commun. 11, 5274 (2020).

    PubMed  PubMed Central  ADS  Google Scholar 

  79. Weider, L. J., Jeyasingh, P. D. & Frisch, D. Evolutionary aspects of resurrection ecology: progress, scope, and applications — an overview. Evol. Appl. 11, 3–10 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. Kawecki, T. J. et al. Experimental evolution. Trends Ecol. Evol. 27, 547–560 (2012). Excellent review paper on main strengths and weaknesses of experimental evolution.

    PubMed  Google Scholar 

  81. Kofler, R. & Schlötterer, C. A guide for the design of evolve and resequencing studies. Mol. Biol. Evol. 31, 474–483 (2014). A paper that used simulations to propose guidelines for optimizing design of E&R studies.

    CAS  PubMed  Google Scholar 

  82. Lenski, R. E., Rose, M. R., Simpson, S. C. & Tadler, S. C. Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. Am. Nat. 138, 1315–1341 (1991).

    Google Scholar 

  83. Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003). A landmark review paper establishing E&R experiments as a new field of research developed around the idea of using microorganisms to investigate the dynamics of evolutionary adaptation.

    CAS  PubMed  Google Scholar 

  84. Barrick, J. E. et al. Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243–1247 (2009).

    CAS  PubMed  ADS  Google Scholar 

  85. Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  86. Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).

    PubMed  PubMed Central  ADS  Google Scholar 

  87. Lenski, R. E. Revisiting the design of the long-term evolution experiment with Escherichia coli. J. Mol. Evol. 91, 241–253 (2023).

    CAS  PubMed  ADS  Google Scholar 

  88. Barghi, N. et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS Biol. 17, e3000128 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015). A comprehensive paper that reviews the field of E&R experiments across diverse systems, ranging from simple non-living RNA to bacteria, yeast and Drosophila sp.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Schlötterer, C., Kofler, R., Versace, E., Tobler, R. & Franssen, S. Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation. Heredity 114, 431–440 (2015).

    PubMed  Google Scholar 

  91. Schlötterer, C., Tobler, R., Kofler, R. & Nolte, V. Sequencing pools of individuals—mining genome-wide polymorphism data without big funding. Nat. Rev. Genet. 15, 749–763 (2014).

    PubMed  Google Scholar 

  92. Huang, C.-J., Lu, M.-Y., Chang, Y.-W. & Li, W.-H. Experimental evolution of yeast for high-temperature tolerance. Mol. Biol. Evol. 35, 1823–1839 (2018).

    CAS  PubMed  Google Scholar 

  93. Otte, K. A., Nolte, V., Mallard, F. & Schlötterer, C. The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime. Genome Biol. 22, 211 (2021).

    PubMed  PubMed Central  Google Scholar 

  94. Burny, C., Nolte, V., Dolezal, M. & Schlötterer, C. Genome-wide selection signatures reveal widespread synergistic effects of two different stressors in Drosophila melanogaster. Proc. Biol. Sci. 289, 20221857 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Orozco‐Terwengel, P. et al. Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Mol. Ecol. 21, 4931–4941 (2012).

    PubMed  PubMed Central  Google Scholar 

  96. Tobler, R. et al. Massive habitat-specific genomic response in D. melanogaster populations during experimental evolution in hot and cold environments. Mol. Biol. Evol. 31, 364–375 (2014).

    CAS  PubMed  Google Scholar 

  97. Brennan, R. S., Garrett, A. D., Huber, K. E., Hargarten, H. & Pespeni, M. H. Rare genetic variation and balanced polymorphisms are important for survival in global change conditions. Proc. R. Soc. B. 286, 20190943 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  99. Waldvogel, A. M. et al. The genomic footprint of climate adaptation in Chironomus riparius. Mol. Ecol. 27, 1439–1456 (2018).

    PubMed  Google Scholar 

  100. Mérot, C., Llaurens, V., Normandeau, E., Bernatchez, L. & Wellenreuther, M. Balancing selection via life-history trade-offs maintains an inversion polymorphism in a seaweed fly. Nat. Commun. 11, 670 (2020).

    PubMed  PubMed Central  ADS  Google Scholar 

  101. Hsu, S. K., Belmouaden, C., Nolte, V. & Schlötterer, C. Parallel gene expression evolution in natural and laboratory evolved populations. Mol. Ecol. 30, 884–894 (2021).

    CAS  PubMed  Google Scholar 

  102. Pfenninger, M. & Foucault, Q. Genomic processes underlying rapid adaptation of a natural Chironomus riparius population to unintendedly applied experimental selection pressures. Mol. Ecol. 29, 536–548 (2020).

    CAS  PubMed  Google Scholar 

  103. Orsini, L. et al. The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments. Trends Ecol. Evol. 28, 274–282 (2013). An excellent, early review on how combining resurrection ecology and genomics can enhance capacity to forecast how populations can adapt to changing environments.

    PubMed  PubMed Central  Google Scholar 

  104. Kerfoot, W. C., Robbins, J. A. & Weider, L. J. A new approach to historical reconstruction: combining descriptive and experimental paleolimnology. Limnol. Oceanogr. 44, 1232–1247 (1999).

    ADS  Google Scholar 

  105. Kerfoot, W. C. & Weider, L. J. Experimental paleoecology (resurrection ecology): chasing Van Valen’s Red Queen hypothesis. Limnol. Oceanogr. 49, 1300–1316 (2004).

    ADS  Google Scholar 

  106. Franks, S. J. et al. The resurrection initiative: storing ancestral genotypes to capture evolution in action. Bioscience 58, 870–873 (2008).

    Google Scholar 

  107. Franks, S. J., Kane, N. C., O’Hara, N. B., Tittes, S. & Rest, J. S. Rapid genome‐wide evolution in Brassica rapa populations following drought revealed by sequencing of ancestral and descendant gene pools. Mol. Ecol. 25, 3622–3631 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Franks, S. J. & Hoffmann, A. A. Genetics of climate change adaptation. Annu. Rev. Genet. 46, 185–208 (2012). This early, comprehensive review paper covers in detail integrative approaches towards elucidating the genetic basis of adaptation.

    CAS  PubMed  Google Scholar 

  109. Orsini, L., Spanier, K. I. & De Meester, L. Genomic signature of natural and anthropogenic stress in wild populations of the waterflea Daphnia magna: validation in space, time and experimental evolution. Mol. Ecol. 21, 2160–2175 (2012).

    PubMed  Google Scholar 

  110. Orsini, L. et al. Temporal genetic stability in natural populations of the waterflea Daphnia magna in response to strong selection pressure. Mol. Ecol. 25, 6024–6038 (2016).

    PubMed  Google Scholar 

  111. Franks, S. J., Hamann, E. & Weis, A. E. Using the resurrection approach to understand contemporary evolution in changing environments. Evol. Appl. 11, 17–28 (2018).

    PubMed  Google Scholar 

  112. Chaturvedi, A. et al. Extensive standing genetic variation from a small number of founders enables rapid adaptation in Daphnia. Nat. Commun. 12, 4306 (2021).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  113. Wersebe, M. J. & Weider, L. J. Resurrection genomics provides molecular and phenotypic evidence of rapid adaptation to salinization in a keystone aquatic species. Proc. Natl Acad. Sci. USA 120, e2217276120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Cuenca Cambronero, M., Zeis, B. & Orsini, L. Haemoglobin‐mediated response to hyper‐thermal stress in the keystone species Daphnia magna. Evol. Appl. 11, 112–120 (2018).

    CAS  PubMed  Google Scholar 

  115. Exposito-Alonso, M. et al. The rate and potential relevance of new mutations in a colonizing plant lineage. PLoS Genet. 14, e1007155 (2018).

    PubMed  PubMed Central  Google Scholar 

  116. Hamann, E. et al. Plant eco-evolutionary responses to climate change: emerging directions. Plant Sci. 304, 110737 (2021).

    CAS  PubMed  Google Scholar 

  117. Frisch, D. et al. A millennial-scale chronicle of evolutionary responses to cultural eutrophication in Daphnia. Ecol. Lett. 17, 360–368 (2014).

    PubMed  Google Scholar 

  118. Hamann, E. et al. Rapid evolutionary changes in gene expression in response to climate fluctuations. Mol. Ecol. 30, 193–206 (2021).

    PubMed  Google Scholar 

  119. Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).

    CAS  PubMed  ADS  Google Scholar 

  120. Campbell-Staton, S. C. et al. Winter storms drive rapid phenotypic, regulatory, and genomic shifts in the green anole lizard. Science 357, 495–498 (2017).

    CAS  PubMed  ADS  Google Scholar 

  121. Jensen, E. L. & Leigh, D. M. Using temporal genomics to understand contemporary climate change responses in wildlife. Ecol. Evol. 12, e9340 (2022).

    PubMed  PubMed Central  Google Scholar 

  122. Clark, R. D. et al. The practice and promise of temporal genomics for measuring evolutionary responses to global change. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13789 (2023).

    Article  PubMed  Google Scholar 

  123. Elleouet, J. S. & Aitken, S. N. The interplay between demography and neutral evolution at the expansion front of a widespread conifer, Picea sitchensis. Preprint at bioRxiv https://doi.org/10.1101/327742 (2018).

  124. Lang, P. L. M., Willems, F. M., Scheepens, J. F., Burbano, H. A. & Bossdorf, O. Using herbaria to study global environmental change. Nat. Phytol. 221, 110–122 (2019).

    Google Scholar 

  125. Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C. R. Rapid evolution in salmon life history induced by direct and indirect effects of fishing. Science 376, 420–423 (2022).

    CAS  PubMed  ADS  Google Scholar 

  126. Buffalo, V. & Coop, G. The linked selection signature of rapid adaptation in temporal genomic data. Genetics 213, 1007–1045 (2019).

    PubMed  PubMed Central  Google Scholar 

  127. Foll, M., Shim, H. & Jensen, J. D. WFABC: a Wright–Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data. Mol. Ecol. Resour. 15, 87–98 (2015).

    PubMed  Google Scholar 

  128. Therkildsen, N. O. et al. Spatiotemporal SNP analysis reveals pronounced biocomplexity at the northern range margin of Atlantic cod Gadus morhua. Evol. Appl. 6, 690–705 (2013).

    PubMed  PubMed Central  Google Scholar 

  129. Anderson, J. T., Panetta, A. M. & Mitchell-Olds, T. Evolutionary and ecological responses to anthropogenic climate change: update on anthropogenic climate change. Plant Physiol. 160, 1728–1740 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. DeBiasse, M. B. & Kelly, M. W. Plastic and evolved responses to global change: what can we learn from comparative transcriptomics? J. Hered. 107, 71–81 (2016).

    PubMed  Google Scholar 

  131. Oomen, R. A. & Hutchings, J. A. Transcriptomic responses to environmental change in fishes: insights from RNA sequencing. Facets 2, 610–641 (2017).

    Google Scholar 

  132. Hu, J. & Barrett, R. Epigenetics in natural animal populations. J. Evol. Biol. 30, 1612–1632 (2017).

    CAS  PubMed  Google Scholar 

  133. McCaw, B. A., Stevenson, T. J. & Lancaster, L. T. Epigenetic responses to temperature and climate. Integr. Comp. Biol. 60, 1469–1480 (2020).

    CAS  PubMed  Google Scholar 

  134. Abdelnour, S. A. et al. Stress biomarkers and proteomics alteration to thermal stress in ruminants: a review. J. Therm. Biol. 79, 120–134 (2019).

    CAS  PubMed  Google Scholar 

  135. Anastasiadi, D., Venney, C. J., Bernatchez, L. & Wellenreuther, M. Epigenetic inheritance and reproductive mode in plants and animals. Trends Ecol. Evol. 36, 1124–1140 (2021).

    PubMed  Google Scholar 

  136. Ecker, S., Pancaldi, V., Valencia, A., Beck, S. & Paul, D. S. Epigenetic and transcriptional variability shape phenotypic plasticity. BioEssays 40, 1700148 (2018).

    Google Scholar 

  137. O’Dea, R. E., Noble, D. W., Johnson, S. L., Hesselson, D. & Nakagawa, S. The role of non-genetic inheritance in evolutionary rescue: epigenetic buffering, heritable bet hedging and epigenetic traps. Environ. Epigenet. 2, dvv014 (2016).

    PubMed  PubMed Central  Google Scholar 

  138. Pottier, P. et al. Developmental plasticity in thermal tolerance: ontogenetic variation, persistence, and future directions. Ecol. Lett. 25, 2245–2268 (2022).

    PubMed  PubMed Central  Google Scholar 

  139. Gianella, M., Bradford, K. J. & Guzzon, F. Ecological, (epi) genetic and physiological aspects of bet-hedging in angiosperms. Plant Reprod. 34, 21–36 (2021).

    PubMed  PubMed Central  Google Scholar 

  140. Donelan, S. C. et al. Transgenerational plasticity in human-altered environments. Trends Ecol. Evol. 35, 115–124 (2020).

    PubMed  Google Scholar 

  141. Morris, M. R. & Rogers, S. M. Overcoming maladaptive plasticity through plastic compensation. Curr. Zool. 59, 526–536 (2013).

    Google Scholar 

  142. Hu, J. & Barrett, R. D. The role of plastic and evolved DNA methylation in parallel adaptation of threespine stickleback (Gasterosteus aculeatus). Mol. Ecol. 32, 1581–1591 (2022).

    PubMed  Google Scholar 

  143. Morris, M. R. et al. Gene expression plasticity evolves in response to colonization of freshwater lakes in threespine stickleback. Mol. Ecol. 23, 3226–3240 (2014).

    PubMed  Google Scholar 

  144. Usui, T. et al. The evolution of plasticity at geographic range edges. Trends Ecol. Evol. 38, 831–842 (2023).

    PubMed  Google Scholar 

  145. Bay, R. A. & Palumbi, S. R. Rapid acclimation ability mediated by transcriptome changes in reef-building corals. Genome Biol. Evol. 7, 1602–1612 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. Healy, T. M. & Schulte, P. M. Patterns of alternative splicing in response to cold acclimation in fish. J. Exp. Biol. 222, jeb.193516 (2019).

    Google Scholar 

  147. Pajoro, A., Severing, E., Angenent, G. & Immink, R. Histone H3 lysine 36 methylation affects temperature-induced alternative splicing and flowering in plants. Genome Biol. 18, 102 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Seo, P. J., Park, M.-J. & Park, C.-M. Alternative splicing of transcription factors in plant responses to low temperature stress: mechanisms and functions. Planta 237, 1415–1424 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Thorstensen, M. J., Turko, A. J., Heath, D. D., Jeffries, K. M. & Pitcher, T. E. Acute thermal stress elicits interactions between gene expression and alternative splicing in a fish of conservation concern. J. Exp. Biol. 225, jeb244162 (2022). Differential gene expression and alternative splicing are assessed owing to handling and thermal stress in the imperiled redside dace (Clinostomus elongatus), with important implications for conservation and reintroductions during GCC.

    PubMed  Google Scholar 

  150. Strader, M., Wong, J., Kozal, L., Leach, T. & Hofmann, G. Parental environments alter DNA methylation in offspring of the purple sea urchin, Strongylocentrotus purpuratus. J. Exp. Mar. Biol. Ecol. 517, 54–64 (2019). A controlled epigenetic inheritance experiment showing that parental exposure to oceanic upwelling, a result of GCC, led to altered offspring DNA methylation associated with body size regardless of offspring environment.

    Google Scholar 

  151. Venney, C. J. et al. Thermal regime during parental sexual maturation, but not during offspring rearing, modulates DNA methylation in brook charr (Salvelinus fontinalis). Proc. Biol. Sci. 289, 20220670 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Gugger, P. F., Fitz‐Gibbon, S., PellEgrini, M. & Sork, V. L. Species‐wide patterns of DNA methylation variation in Quercus lobata and their association with climate gradients. Mol. Ecol. 25, 1665–1680 (2016).

    CAS  PubMed  Google Scholar 

  153. Ishihara, A., Sapon, M. A. & Yamauchi, K. Seasonal acclimatization and thermal acclimation induce global histone epigenetic changes in liver of bullfrog (Lithobates catesbeianus) tadpole. Comp. Biochem. Physiol. A 230, 39–48 (2019).

    CAS  Google Scholar 

  154. Hajyzadeh, M., Turktas, M., Khawar, K. M. & Unver, T. miR408 overexpression causes increased drought tolerance in chickpea. Gene 555, 186–193 (2015).

    CAS  PubMed  Google Scholar 

  155. Liu, Q. et al. Integrating small RNA sequencing with QTL mapping for identification of miRNAs and their target genes associated with heat tolerance at the flowering stage in rice. Front. Plant Sci. 8, 43 (2017).

    PubMed  PubMed Central  ADS  Google Scholar 

  156. Weizman, E. & Levy, O. The role of chromatin dynamics under global warming response in the symbiotic coral model Aiptasia. Commun. Biol. 2, 282 (2019). Correlations between RNA expression and promoter chromatin accessibility were found in symbiotic sea anemones exposed to thermal stress in genomic regions associated with oxidative stress and immune response.

    PubMed  PubMed Central  Google Scholar 

  157. Machado, H. E. et al. Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila. eLife 10, e67577 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Liu, Y. et al. Methylation-eQTL analysis in cancer research. Bioinformatics 37, 4014–4022 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Yang, I. V. et al. Relationship of DNA methylation and gene expression in idiopathic pulmonary fibrosis. Am. J. Respir. Crit. Care Med. 190, 1263–1272 (2014).

    PubMed  PubMed Central  Google Scholar 

  160. Dunning, L. T., Dennis, A. B., Sinclair, B. J., Newcomb, R. D. & Buckley, T. R. Divergent transcriptional responses to low temperature among populations of alpine and lowland species of New Zealand stick insects (Micrarchus). Mol. Ecol. 23, 2712–2726 (2014).

    CAS  PubMed  Google Scholar 

  161. Parkinson, J. E. et al. Extensive transcriptional variation poses a challenge to thermal stress biomarker development for endangered corals. Mol. Ecol. 27, 1103–1119 (2018).

    CAS  PubMed  Google Scholar 

  162. Christensen, K. A. et al. Assessing the effects of genotype-by-environment interaction on epigenetic, transcriptomic, and phenotypic response in a Pacific salmon. G3 11, jkab021 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Van Der Graaf, A. et al. Rate, spectrum, and evolutionary dynamics of spontaneous epimutations. Proc. Natl Acad. Sci. USA 112, 6676–6681 (2015).

    PubMed  PubMed Central  ADS  Google Scholar 

  164. Dixon, G., Liao, Y., Bay, L. K. & Matz, M. V. Role of gene body methylation in acclimatization and adaptation in a basal metazoan. Proc. Natl Acad. Sci. USA 115, 13342–13346 (2018).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  165. Wu, Y. et al. Epigenome-wide association study of short-term temperature fluctuations based on within-sibship analyses in Australian females. Environ. Int. 171, 107655 (2023).

    CAS  PubMed  Google Scholar 

  166. Oomen, R. A. & Hutchings, J. A. Genomic reaction norms inform predictions of plastic and adaptive responses to climate change. J. Anim. Ecol. 91, 1073–1087 (2022).

    PubMed  PubMed Central  Google Scholar 

  167. Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).

    PubMed  PubMed Central  Google Scholar 

  168. Lacoursière-Roussel, A., Rosabal, M. & Bernatchez, L. Estimating fish abundance and biomass from eDNA concentrations: variability among capture methods and environmental conditions. Mol. Ecol. Resour. 16, 1401–1414 (2016).

    PubMed  Google Scholar 

  169. Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).

    Google Scholar 

  170. Clare, E. L. et al. eDNAir: proof of concept that animal DNA can be collected from air sampling. PeerJ 9, e11030 (2021).

    PubMed  PubMed Central  Google Scholar 

  171. Roger, F. et al. Airborne environmental DNA metabarcoding for the monitoring of terrestrial insects—a proof of concept from the field. Environ. DNA 4, 790–807 (2022).

    CAS  Google Scholar 

  172. Johnson, M. D., Fokar, M., Cox, R. D. & Barnes, M. A. Airborne environmental DNA metabarcoding detects more diversity, with less sampling effort, than a traditional plant community survey. BMC Ecol. Evol. 21, 218 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. Machado, K. B. et al. DNA metabarcoding reveals the responses of prokaryotes and eukaryotes microbiota to warming: are the patterns similar between taxonomic and trophic groups? Ecol. Indic. 115, 106452 (2020). An experimental approach based on eDNA metabarcoding to evaluate the short-term effect of warming predicted by different future scenarios in the composition of the aquatic microbiota.

    Google Scholar 

  174. Ferguson, R. M. et al. The ecological impacts of multiple environmental stressors on coastal biofilm bacteria. Glob. Change Biol. 27, 3166–3178 (2021).

    CAS  Google Scholar 

  175. Moinet, G. Y. et al. Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Glob. Change Biol. 27, 6217–6231 (2021). A natural long-term warming experiment based on eDNA metabarcoding to study switch in microbial community compositions depending on soil temperature.

    Google Scholar 

  176. Gallego, R., Jacobs-Palmer, E., Cribari, K. & Kelly, R. P. Environmental DNA metabarcoding reveals winners and losers of global change in coastal waters. Proc. Biol. Sci. 287, 20202424 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254 (2020).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  178. Abirami, B., Radhakrishnan, M., Kumaran, S. & Wilson, A. Impacts of global warming on marine microbial communities. Sci. Total Environ. 791, 147905 (2021).

    CAS  PubMed  ADS  Google Scholar 

  179. Thurber, R. V. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).

    CAS  Google Scholar 

  180. Busseni, G. et al. Large scale patterns of marine diatom richness: drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. 29, 1915–1928 (2020).

    Google Scholar 

  181. Diner, R. E. et al. Pathogenic Vibrio species are associated with distinct environmental niches and planktonic taxa in Southern California (USA) aquatic microbiomes. mSystems 6, e0057121 (2021).

    PubMed  Google Scholar 

  182. Wilcox, T. M. et al. Fine‐scale environmental DNA sampling reveals climate‐mediated interactions between native and invasive trout species. Ecosphere 9, e02500 (2018).

    Google Scholar 

  183. Isaak, D. J. et al. Do metapopulations and management matter for relict headwater bull trout populations in a warming climate? Ecol. Appl. 32, e2594 (2022).

    PubMed  Google Scholar 

  184. Balint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).

    PubMed  Google Scholar 

  185. Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).

    PubMed  Google Scholar 

  186. Willerslev, E. et al. Ancient biomolecules from deep ice cores reveal a forested southern Greenland. Science 317, 111–114 (2007).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  187. Wang, Y. et al. Late quaternary dynamics of arctic biota from ancient environmental genomics. Nature 600, 86–92 (2021). A demonstration of the use of aDNA metagenomics to study how past climatic changes affected plant and animal communities.

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  188. Kjær, K. H. et al. A 2-million-year-old ecosystem in Greenland uncovered by environmental DNA. Nature 612, 283–291 (2022).

    PubMed  PubMed Central  ADS  Google Scholar 

  189. Zhang, H., Huo, S., Yeager, K. M. & Wu, F. Sedimentary DNA record of eukaryotic algal and cyanobacterial communities in a shallow Lake driven by human activities and climate change. Sci. Total Environ. 753, 141985 (2021).

    CAS  PubMed  ADS  Google Scholar 

  190. Alsos, I. G. et al. Sedimentary ancient DNA from Lake Skartjørna, Svalbard: assessing the resilience of arctic flora to Holocene climate change. Holocene 26, 627–642 (2016).

    ADS  Google Scholar 

  191. Díaz, F. P. et al. Multiscale climate change impacts on plant diversity in the Atacama Desert. Glob. Change Biol. 25, 1733–1745 (2019).

    ADS  Google Scholar 

  192. Palkopoulou, E. et al. Holarctic genetic structure and range dynamics in the woolly mammoth. Proc. Biol. Sci. 280, 20131910 (2013).

    PubMed  PubMed Central  Google Scholar 

  193. Haile, J. et al. Ancient DNA reveals late survival of mammoth and horse in interior Alaska. Proc. Natl Acad. Sci. USA 106, 22352–22357 (2009).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  194. Willerslev, E. et al. Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506, 47–51 (2014).

    CAS  PubMed  ADS  Google Scholar 

  195. Thomsen, P. F. & Willerslev, E. Environmental DNA – an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).

    Google Scholar 

  196. Hechler, R. M., Yates, M. C., Chain, F. J. & Cristescu, M. E. Environmental transcriptomics under heat stress: can environmental RNA reveal changes in gene expression of aquatic organisms? Preprint at bioRxiv https://doi.org/10.1101/2022.10.06.510878 (2022).

  197. Whitmore, L. et al. Inadvertent human genomic bycatch and intentional capture raise beneficial applications and ethical concerns with environmental DNA. Nat. Ecol. Evol. 7, 873–888 (2023).

    PubMed  PubMed Central  Google Scholar 

  198. Ficetola, G. F. & Taberlet, P. Towards exhaustive community ecology via DNA metabarcoding. Mol. Ecol. https://doi.org/10.1111/mec.16881 (2023).

  199. Chen, Y. et al. The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability. Nat. Commun. 13, 4821 (2022).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  200. Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  201. Tournebize, R. et al. Ecological and genomic vulnerability to climate change across native populations of Robusta coffee (Coffea canephora). Glob. Change Biol. 28, 4124–4142 (2022).

    CAS  Google Scholar 

  202. Sherpa, S. et al. Genomic shifts, phenotypic clines, and fitness costs associated with cold tolerance in the Asian tiger mosquito. Mol. Biol. Evol. 39, 5 (2022).

    Google Scholar 

  203. Forester, B. R., Beever, E. A., Darst, C., Szymanski, J. & Funk, W. C. Linking evolutionary potential to extinction risk: applications and future directions. Front. Ecol. Environ. 20, 507–515 (2022).

    Google Scholar 

  204. Pan, H., Holbrook, J. D., Karnani, N. & Kwoh, C. K. Gene, environment and methylation (GEM): a tool suite to efficiently navigate large scale epigenome wide association studies and integrate genotype and interaction between genotype and environment. BMC Bioinformatics 17, 299 (2016).

    PubMed  PubMed Central  Google Scholar 

  205. Guo, X. et al. Linking genotype to phenotype in multi-omics data of small sample. BMC Genomics 22, 537 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  206. Shi, W. J. et al. Unsupervised discovery of phenotype-specific multi-omics networks. Bioinformatics 35, 4336–4343 (2019).

    PubMed  PubMed Central  Google Scholar 

  207. Hanson, C., Cairns, J., Wang, L. & Sinha, S. Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res. 28, 1207–1216 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  208. Wellenreuther, M. & Bernatchez, L. Eco-evolutionary genomics of chromosomal inversions. Trends Ecol. Evol. 33, 427–440 (2018).

    PubMed  Google Scholar 

  209. Layton, K. K. S. & Bradbury, I. R. Harnessing the power of multi-omics data for predicting climate change response. J. Anim. Ecol. 91, 1064–1072 (2022).

    PubMed  Google Scholar 

  210. Euclide, P. et al. Is structural variation necessary to create islands of divergence in moderate gene flow species? A case study in sockeye salmon. Preprint at Authorea https://doi.org/10.22541/au.168371520.09492745/v1 (2023).

  211. Wellenreuther, M., Mérot, C., Berdan, E. & Bernatchez, L. Going beyond SNPs: the role of structural genomic variants in adaptive evolution and species diversification. Mol. Ecol. 28, 1203–1209 (2019).

    PubMed  Google Scholar 

  212. Hamann, E. et al. Review: plant eco-evolutionary responses to climate change: emerging directions. Plant Sci. 304, 110737 (2021).

    CAS  PubMed  Google Scholar 

  213. Ferchiou, S., Caza, F., de Boissel, P. G. J., Villemur, R. & St-Pierre, Y. Applying the concept of liquid biopsy to monitor the microbial biodiversity of marine coastal ecosystems. ISME Commun. 2, 61 (2022).

    PubMed  PubMed Central  Google Scholar 

  214. Valdivieso, A., Anastasiadi, D., Ribas, L. & Piferrer, F. Development of epigenetic biomarkers for the identification of sex and thermal stress in fish using DNA methylation analysis and machine learning procedures. Mol. Ecol. Resour. 23, 453–470 (2023).

    CAS  PubMed  Google Scholar 

  215. Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

    CAS  PubMed  Google Scholar 

  216. Tumajer, J. et al. Forward modeling reveals multidecadal trends in cambial kinetics and phenology at treeline. Front. Plant Sci. 12, 613643 (2021).

    PubMed  PubMed Central  Google Scholar 

  217. Xuereb, A., Rougemont, Q., Tiffin, P., Xue, H. & Phifer-Rixey, M. Individual-based eco-evolutionary models for understanding adaptation in changing seas. Proc. Biol. Sci. 288, 20212006 (2021).

    PubMed  PubMed Central  Google Scholar 

  218. Carley, L. N., Morris, W. F., Walsh, R., Riebe, D. & Mitchell‐Olds, T. Are genetic variation and demographic performance linked? Evol. Appl. 15, 1888–1906 (2022).

    PubMed  PubMed Central  Google Scholar 

  219. Mathon, L. et al. The distribution of coastal fish eDNA sequences in the Anthropocene. Glob. Ecol. Biogeogr. 32, 1336–1352 (2023).

    Google Scholar 

  220. Lewin, H. A. et al. The earth BioGenome project 2020: starting the clock. Proc. Natl Acad. Sci. USA 119, e2115635118 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  221. Jenkins, G. B. et al. Reproducibility in ecology and evolution: minimum standards for data and code. Ecol. Evol. 13, e9961 (2023).

    PubMed  PubMed Central  Google Scholar 

  222. Dauphin, B. et al. Re-thinking the environment in landscape genomics. Trends Ecol. Evol. 38, 261–274 (2023).

    CAS  PubMed  Google Scholar 

  223. Waldvogel, A.-M. & Pfenninger, M. Temperature dependence of spontaneous mutation rates. Genome Res. 31, 1582–1589 (2021).

    PubMed  PubMed Central  Google Scholar 

  224. Leigh, D. M. et al. Opportunities and challenges of macrogenetic studies. Nat. Rev. Genet. 22, 791–807 (2021).

    CAS  PubMed  Google Scholar 

  225. Schmidt, C., Hoban, S. & Jetz, W. Conservation macrogenetics: harnessing genetic data to meet conservation commitments. Trends Genet. https://doi.org/10.1016/j.tig.2023.08.002 (2023).

  226. Hoban, S. et al. Global genetic diversity status and trends: towards a suite of essential biodiversity variables (EBVs) for genetic composition. Biol. Rev. 97, 1511–1538 (2022).

    PubMed  Google Scholar 

  227. Hoban, S. et al. Genetic diversity goals and targets have improved, but remain insufficient for clear implementation of the post-2020 global biodiversity framework. Conserv. Genet. 24, 181–191 (2023).

    PubMed  PubMed Central  Google Scholar 

  228. Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).

    Google Scholar 

  229. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Google Scholar 

  230. McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).

    PubMed  Google Scholar 

  231. Excoffier, L. & Foll, M. Fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics 27, 1332–1334 (2011).

    CAS  PubMed  Google Scholar 

  232. Deatherage, D. E., Kepner, J. L., Bennett, A. F., Lenski, R. E. & Barrick, J. E. Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures. Proc. Natl Acad. Sci. USA 114, E1904–E1912 (2017).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  233. Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 151, 165–188 (2014).

    Google Scholar 

  234. Lenski, R. E. Convergence and divergence in a long-term experiment with bacteria. Am. Nat. 190, S57–S68 (2017).

    PubMed  Google Scholar 

  235. Jansen, M. et al. Thermal tolerance in the keystone species Daphnia magna — a candidate gene and an outlier analysis approach. Mol. Ecol. 26, 2291–2305 (2017).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by an NSERC Discovery grant to L.B. C.J.V. is supported by an NSERC postdoctoral fellowship.

Author information

Author notes

  1. Deceased: Louis Bernatchez.

Authors and Affiliations

  1. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada

    Louis Bernatchez, Anne-Laure Ferchaud, Chloé Suzanne Berger, Clare J. Venney & Amanda Xuereb

  2. Parks Canada, Office of the Chief Ecosystem Scientist, Protected Areas Establishment, Quebec City, Quebec, Canada

    Anne-Laure Ferchaud

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  1. Louis Bernatchez
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The authors contributed equally to all aspects of the article.

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Correspondence to Anne-Laure Ferchaud.

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Dedication

We wish to dedicate this review to the memory of Louis Bernatchez, who passed away shortly after its acceptance. We are all deeply grateful to Louis, not only for conceiving this piece of work, but most importantly for his outstanding contributions to the field, invaluable mentorship, and boundless generosity and compassion.

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Glossary

Assisted gene flow

Human-mediated movement of individuals within a species range to facilitate adaptation in recipient populations.

Conservation macrogenetics

Studies of intraspecific genetic variation across many taxonomic groups and broad spatiotemporal scales to assess evolutionary patterns and inform conservation.

Ecological niche model

(ENM). Sometimes referred to as environmental niche model or species distribution model. A class of methods that combines environmental data layers and species’ occurrence or abundance data to infer niche suitability and predict species’ distributions across current and future landscapes.

Epigenome-wide association studies

(EWAS). Statistical method that links epigenomic variation to phenotypic or physiological outcomes.

Evolve and resequence

(E&R). Integrates high-throughput sequencing into experimental evolution studies; most commonly performed on microbial and short-generation metazoans.

Generalized dissimilarity modelling

(GDM). A statistical approach for prediction of nonlinear patterns of beta diversity across space; although initially developed for modelling turnover in community composition, it can also be applied to model genetic and trait differences.

Genome scans

A genome-wide screening approach to identify candidate genomic variants (for example, SNPs) that are subject to selection.

Genomic reaction norm

Graphical representations showing how a phenotype (generally gene expression, although other molecular phenotypes such as DNA methylation state can be used) of a single genotype changes in response to different environments.

Individual-based modelling

(IBM). Modelling approach in which the actions of individual organisms are simulated, including interactions with other individuals and with the environment.

Integral projection models

Models that predict the effects of individuals’ traits (for example, age, reproductive success, growth rate, body size) on population size and demography.

Landscape genomics

The study of the spatial distribution of genome-wide variation, including both neutral and adaptive variation, across heterogeneous landscapes and the processes that shape observed distributions.

Landscape resistance

A measure of the difficulty for organisms to move through the landscape; areas with high resistance can impede dispersal and gene flow.

Liquid biopsy

Noninvasive test to detect noncellular DNA in bodily fluids, often used for cancer and disease detection.

Local adaptation

Mechanism by which organisms in a population evolve phenotypes that have higher relative fitness in their home conditions compared with organisms in other populations in spatially heterogeneous environments.

Nonsense substitutions

Point mutations in a sequence of DNA that result in a premature stop codon.

Non-synonymous substitutions

Nucleotide mutation that alter the amino acid sequence of a protein.

Otoliths

Calcium carbonate structures in the inner ear of vertebrates.

Quantitative trait loci

(QTLs). Genetic variants associated with changes in expression (eQTL) or DNA methylation (methylQTL) levels.

Rescue effects

An increase in population size or fitness, thereby reducing the risk of extinction and facilitating long-term population persistence. Three eco-evolutionary processes promote rescue effects: demographic rescue, that is, an increase in the number of individuals in a population; genetic rescue, that is, an increase in population fitness via the genetic contribution of immigrants, by either decreasing inbreeding depression or increasing genetic variation to promote adaptation; evolutionary rescue, that is, an increase in population growth rates caused by adaptation to new or changing conditions, usually from standing genetic variation but some definitions include migration.

Standing genetic variation

Pre-existing genetic variation in a population on which selection can act readily.

Structural variation

A region of the genome that shows inter-individual variability due to a chromosomal alteration, including variation in chromosomal location (for example, translocation), rearrangement of chromosome orientation (for example, inversion) or copy number variation (for example, duplications, insertions, deletions).

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Bernatchez, L., Ferchaud, AL., Berger, C.S. et al. Genomics for monitoring and understanding species responses to global climate change. Nat Rev Genet 25, 165–183 (2024). https://doi.org/10.1038/s41576-023-00657-y

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