1932

Abstract

Insects constitute vital components of ecosystems. There is alarming evidence for global declines in insect species diversity, abundance, and biomass caused by anthropogenic drivers such as habitat degradation or loss, agricultural practices, climate change, and environmental pollution. This raises important concerns about human food security and ecosystem functionality and calls for more research to assess insect population trends and identify threatened species and the causes of declines to inform conservation strategies. Analysis of genetic diversity is a powerful tool to address these goals, but so far animal conservation genetics research has focused strongly on endangered vertebrates, devoting less attention to invertebrates, such as insects, that constitute most biodiversity. Insects’ shorter generation times and larger population sizes likely necessitate different analytical methods and management strategies. The availability of high-quality reference genome assemblies enables population genomics to address several key issues. These include precise inference of past demographic fluctuations and recent declines, measurement of genetic load levels, delineation of evolutionarily significant units and cryptic species, and analysis of genetic adaptation to stressors. This enables identification of populations that are particularly vulnerable to future threats, considering their potential to adapt and evolve. We review the application of population genomics to insect conservation and the outlook for averting insect declines.

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2023-02-15
2024-12-14
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Literature Cited

  1. 1.
    Supple MA, Shapiro B. 2018. Conservation of biodiversity in the genomics era. Genome Biol. 19:131
    [Google Scholar]
  2. 2.
    Hohenlohe PA, Funk WC, Rajora OP. 2021. Population genomics for wildlife conservation and management. Mol. Ecol. 30:16282
    [Google Scholar]
  3. 3.
    Primmer CR. 2009. From conservation genetics to conservation genomics. Ann. N.Y. Acad. Sci. 1162:35768
    [Google Scholar]
  4. 4.
    Allendorf FW, Hohenlohe PA, Luikart G. 2010. Genomics and the future of conservation genetics. Nat. Rev. Genet. 11:10697709
    [Google Scholar]
  5. 5.
    Ouborg NJ, Pertoldi C, Loeschcke V, Bijlsma RK, Hedrick PW. 2010. Conservation genetics in transition to conservation genomics. Trends Genet. 26:417787
    [Google Scholar]
  6. 6.
    Wagner DL. 2020. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65:45780Balanced and comprehensive summary of the evidence for insect declines and their drivers.
    [Google Scholar]
  7. 7.
    Sánchez-Bayo F, Wyckhuys KAG. 2019. Worldwide decline of the entomofauna: a review of its drivers. Biol. Conserv. 232:827
    [Google Scholar]
  8. 8.
    Hallmann CA, Sorg M, Jongejans E, Siepel H, Hofland N et al. 2017. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLOS ONE 12:10e0185809
    [Google Scholar]
  9. 9.
    Seibold S, Gossner MM, Simons NK, Blüthgen N, Müller J et al. 2019. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574:778067174
    [Google Scholar]
  10. 10.
    van Klink R, Bowler DE, Gongalsky KB, Swengel AB, Gentile A, Chase JM. 2020. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368:648941720
    [Google Scholar]
  11. 11.
    Dirzo R, Young HS, Galetti M, Ceballos G, Isaac NJB, Collen B 2014. Defaunation in the Anthropocene. Science 345:61954016
    [Google Scholar]
  12. 12.
    IPBES 2019. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services ES Brondizio, J Settele, S Díaz, HT Ngo Bonn, Ger.: IPBES Secr.
    [Google Scholar]
  13. 13.
    Scheffers BR, Joppa LN, Pimm SL, Laurance WF. 2012. What we know and don't know about Earth's missing biodiversity. Trends Ecol. Evol. 27:950110
    [Google Scholar]
  14. 14.
    Stork NE. 2018. How many species of insects and other terrestrial arthropods are there on earth?. Annu. Rev. Entomol. 63:3145
    [Google Scholar]
  15. 15.
    Bowler DE, Heldbjerg H, Fox AD, de Jong M, Böhning-Gaese K. 2019. Long-term declines of European insectivorous bird populations and potential causes. Conserv. Biol. 33:5112030
    [Google Scholar]
  16. 16.
    Lister BC, Garcia A. 2018. Climate-driven declines in arthropod abundance restructure a rainforest food web. PNAS 115:44E10397406
    [Google Scholar]
  17. 17.
    Powney GD, Carvell C, Edwards M, Morris RKA, Roy HE et al. 2019. Widespread losses of pollinating insects in Britain. Nat. Commun. 10:1018
    [Google Scholar]
  18. 18.
    Goulson D, Nicholls E, Botías C, Rotheray EL. 2015. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347:62291255957
    [Google Scholar]
  19. 19.
    Potts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, Kunin WE. 2010. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25:34553
    [Google Scholar]
  20. 20.
    Biesmeijer JC, Roberts SPM, Reemer M, Ohlemüller R, Edwards M et al. 2006. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:578535154
    [Google Scholar]
  21. 21.
    Thomas JA, Telfer MG, Roy DB, Preston CD, Greenwood JJD et al. 2004. Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303:5665187981
    [Google Scholar]
  22. 22.
    Franzén M, Johannesson M. 2007. Predicting extinction risk of butterflies and moths (Macrolepidoptera) from distribution patterns and species characteristics. J. Insect Conserv. 11:436790
    [Google Scholar]
  23. 23.
    Conrad KF, Warren MS, Fox R, Parsons MS, Woiwod IP. 2006. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132:327991
    [Google Scholar]
  24. 24.
    Neumann P, Carreck NL. 2010. Honey bee colony losses. J. Apic. Res. 49:116
    [Google Scholar]
  25. 25.
    Kerr JT, Pindar A, Galpern P, Packer L, Potts SG et al. 2015. Climate change impacts on bumblebees converge across continents. Science 349:624417780
    [Google Scholar]
  26. 26.
    Bartomeus I, Ascher JS, Gibbs J, Danforth BN, Wagner DL et al. 2013. Historical changes in northeastern US bee pollinators related to shared ecological traits. PNAS 110:12465660
    [Google Scholar]
  27. 27.
    Wagner DL. 2019. Global insect decline: comments on Sánchez-Bayo and Wyckhuys 2019. Biol. Conserv. 233:33233
    [Google Scholar]
  28. 28.
    Thomas CD, Jones TH, Hartley SE. 2019. “Insectageddon”: a call for more robust data and rigorous analyses. Glob. Change Biol. 25:6189192
    [Google Scholar]
  29. 29.
    Pilotto F, Kühn I, Adrian R, Alber R, Alignier A et al. 2020. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 11:3486
    [Google Scholar]
  30. 30.
    Crossley MS, Meier AR, Baldwin EM, Berry LL, Crenshaw LC et al. 2020. No net insect abundance and diversity declines across US Long Term Ecological Research sites. Nat. Ecol. Evol. 4:10136876
    [Google Scholar]
  31. 31.
    Welti EAR, Joern A, Ellison AM, Lightfoot DC, Record S et al. 2021. Studies of insect temporal trends must account for the complex sampling histories inherent to many long-term monitoring efforts. Nat. Ecol. Evol. 5:558991
    [Google Scholar]
  32. 32.
    Wagner DL, Grames EM, Forister ML, Berenbaum MR, Stopak D. 2021. Insect decline in the Anthropocene: death by a thousand cuts. PNAS 118:2e2023989118
    [Google Scholar]
  33. 33.
    Outhwaite CL, McCann P, Newbold T. 2022. Agriculture and climate change are reshaping insect biodiversity worldwide. Nature 605:790897102
    [Google Scholar]
  34. 34.
    Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA et al. 2013. High-resolution global maps of 21st-century forest cover change. Science 342:616085053
    [Google Scholar]
  35. 35.
    Carrasco LR, Webb EL, Symes WS, Koh LP, Sodhi NS. 2017. Global economic trade-offs between wild nature and tropical agriculture. PLOS Biol. 15:7e2001657
    [Google Scholar]
  36. 36.
    Zanon M, Davis BAS, Marquer L, Brewer S, Kaplan JO. 2018. European forest cover during the past 12,000 years: a palynological reconstruction based on modern analogs and remote sensing. Front. Plant Sci. 9:253
    [Google Scholar]
  37. 37.
    Kaplan JO, Krumhardt KM, Zimmermann N. 2009. The prehistoric and preindustrial deforestation of Europe. Quat. Sci. Rev. 28:27301634
    [Google Scholar]
  38. 38.
    Kaplan JO, Krumhardt KM, Gaillard M-J, Sugita S, Trondman A-K et al. 2017. Constraining the deforestation history of Europe: evaluation of historical land use scenarios with pollen-based land cover reconstructions. Land 6:491
    [Google Scholar]
  39. 39.
    Botías C, David A, Hill EM, Goulson D. 2017. Quantifying exposure of wild bumblebees to mixtures of agrochemicals in agricultural and urban landscapes. Environ. Pollut. 222:7382
    [Google Scholar]
  40. 40.
    Ollerton J, Erenler H, Edwards M, Crockett R. 2014. Extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346:6215136062
    [Google Scholar]
  41. 41.
    Henry M, Béguin M, Requier F, Rollin O, Odoux J-F et al. 2012. A common pesticide decreases foraging success and survival in honey bees. Science 336:607934850
    [Google Scholar]
  42. 42.
    Gill RJ, Ramos-Rodriguez O, Raine NE 2012. Combined pesticide exposure severely affects individual- and colony-level traits in bees. Nature 491:74221058
    [Google Scholar]
  43. 43.
    Whitehorn PR, O'Connor S, Wackers FL, Goulson D 2012. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336:607935152
    [Google Scholar]
  44. 44.
    Rundlöf M, Andersson GKS, Bommarco R, Fries I, Hederström V et al. 2015. Seed coating with a neonicotinoid insecticide negatively affects wild bees. Nature 521:75507780Provides evidence that wild bees are negatively impacted by treatment of nearby crops with neonicotinoid pesticides at realistic doses.
    [Google Scholar]
  45. 45.
    Goulet H, Masner L. 2017. Impact of herbicides on the insect and spider diversity in eastern Canada. Biodiversity 18:2–35057
    [Google Scholar]
  46. 46.
    Öckinger E, Hammarstedt O, Nilsson SG, Smith HG. 2006. The relationship between local extinctions of grassland butterflies and increased soil nitrogen levels. Biol. Conserv. 128:456473
    [Google Scholar]
  47. 47.
    Soroye P, Newbold T, Kerr J. 2020. Climate change contributes to widespread declines among bumble bees across continents. Science 367:647868588
    [Google Scholar]
  48. 48.
    Breed GA, Stichter S, Crone EE. 2013. Climate-driven changes in northeastern US butterfly communities. Nat. Clim. Change 3:214245
    [Google Scholar]
  49. 49.
    Rasmont P, Franzén M, Lecocq T, Harpke A, Roberts SPM et al. 2015. Climatic risk and distribution atlas of European bumblebees. BioRisk 10:1236
    [Google Scholar]
  50. 50.
    Forister ML, Novotny V, Panorska AK, Baje L, Basset Y et al. 2015. The global distribution of diet breadth in insect herbivores. PNAS 112:244247
    [Google Scholar]
  51. 51.
    Gérard M, Vanderplanck M, Wood T, Michez D. 2020. Global warming and plant-pollinator mismatches. Emerg. . Top. Life Sci. 4:17786
    [Google Scholar]
  52. 52.
    Miller-Struttmann NE, Geib JC, Franklin JD, Kevan PG, Holdo RM et al. 2015. Functional mismatch in a bumble bee pollination mutualism under climate change. Science 349:6255154144
    [Google Scholar]
  53. 53.
    Cornelissen B, Neumann P, Schweiger O. 2019. Global warming promotes biological invasion of a honey bee pest. Glob. Change Biol. 25:11364255
    [Google Scholar]
  54. 54.
    Salih AAM, Baraibar M, Mwangi KK, Artan G. 2020. Climate change and locust outbreak in East Africa. Nat. Clim. Change 10:758485
    [Google Scholar]
  55. 55.
    Wagner DL, Van Driesche RG. 2010. Threats posed to rare or endangered insects by invasions of nonnative species. Annu. Rev. Entomol. 55:54768
    [Google Scholar]
  56. 56.
    Bertelsmeier C, Ollier S, Liebhold A, Keller L. 2017. Recent human history governs global ant invasion dynamics. Nat. Ecol. Evol. 1:0184
    [Google Scholar]
  57. 57.
    Fürst MA, McMahon DP, Osborne JL, Paxton RJ, Brown MJF. 2014. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506:748836466
    [Google Scholar]
  58. 58.
    Proesmans W, Albrecht M, Gajda A, Neumann P, Paxton RJ et al. 2021. Pathways for novel epidemiology: plant-pollinator-pathogen networks and global change. Trends Ecol. Evol. 36:762336
    [Google Scholar]
  59. 59.
    Arbetman MP, Meeus I, Morales CL, Aizen MA, Smagghe G. 2013. Alien parasite hitchhikes to Patagonia on invasive bumblebee. Biol. Invasions 15:348994
    [Google Scholar]
  60. 60.
    Vilcinskas A. 2019. Pathogens associated with invasive or introduced insects threaten the health and diversity of native species. Curr. Opin. Insect Sci. 33:4348
    [Google Scholar]
  61. 61.
    Romiguier J, Gayral P, Ballenghien M, Bernard A, Cahais V et al. 2014. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515:752626163Evidence that levels of genetic diversity across mammals are determined mainly by intrinsic life-history traits.
    [Google Scholar]
  62. 62.
    Leffler EM, Bullaughey K, Matute DR, Meyer WK, Ségurel L et al. 2012. Revisiting an old riddle: What determines genetic diversity levels within species?. PLOS Biol. 10:9e1001388
    [Google Scholar]
  63. 63.
    Willi Y, Kristensen TN, Sgrò CM, Weeks AR, Ørsted M, Hoffmann AA. 2022. Conservation genetics as a management tool: the five best-supported paradigms to assist the management of threatened species. PNAS 119:1e2105076119
    [Google Scholar]
  64. 64.
    Husemann M, Zachos FE, Paxton RJ, Habel JC. 2016. Effective population size in ecology and evolution. Heredity 117:419192
    [Google Scholar]
  65. 65.
    Teixeira JC, Huber CD. 2021. The inflated significance of neutral genetic diversity in conservation genetics. PNAS 118:10e2014096118
    [Google Scholar]
  66. 66.
    Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW et al. 2021. The crucial role of genome-wide genetic variation in conservation. PNAS 118:48e2104642118
    [Google Scholar]
  67. 67.
    Genereux DP, Serres A, Armstrong J, Johnson J, Marinescu VD et al. 2020. A comparative genomics multitool for scientific discovery and conservation. Nature 587:783324045
    [Google Scholar]
  68. 68.
    Perry GH, Melsted P, Marioni JC, Wang Y, Bainer R et al. 2012. Comparative RNA sequencing reveals substantial genetic variation in endangered primates. Genome Res. 22:460210
    [Google Scholar]
  69. 69.
    Ellegren H, Galtier N. 2016. Determinants of genetic diversity. Nat. Rev. Genet. 17:742233
    [Google Scholar]
  70. 70.
    Spielman D, Brook BW, Frankham R. 2004. Most species are not driven to extinction before genetic factors impact them. PNAS 101:421526164
    [Google Scholar]
  71. 71.
    Tscharntke T, Steffan-Dewenter I, Kruess A, Thies C. 2002. Characteristics of insect populations on habitat fragments: a mini review. Ecol. Res. 17:222939
    [Google Scholar]
  72. 72.
    Hoffmann AA, White VL, Jasper M, Yagui H, Sinclair SJ, Kearney MR. 2021. An endangered flightless grasshopper with strong genetic structure maintains population genetic variation despite extensive habitat loss. Ecol. Evol. 11:10536480
    [Google Scholar]
  73. 73.
    Resh VH, Cardé RT, eds. 2009. Encyclopedia of Insects Amsterdam: Academic. , 2nd ed..
    [Google Scholar]
  74. 74.
    Romiguier J, Lourenco J, Gayral P, Faivre N, Weinert LA et al. 2014. Population genomics of eusocial insects: the costs of a vertebrate-like effective population size. J. Evol. Biol. 27:3593603
    [Google Scholar]
  75. 75.
    Weyna A, Romiguier J. 2021. Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. Peer Community J. 1:e2
    [Google Scholar]
  76. 76.
    Capblancq T, Fitzpatrick MC, Bay RA, Exposito-Alonso M, Keller SR. 2020. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51:24569
    [Google Scholar]
  77. 77.
    Paez S, Kraus RHS, Shapiro B, Gilbert MTP, Jarvis ED, Vert. Genomes Proj. Consort. 2022. Reference genomes for conservation. Science 377:660436466
    [Google Scholar]
  78. 78.
    Lewin HA, Richards S, Lieberman Aiden E, Allende ML, Archibald JM et al. 2022. The Earth BioGenome Project 2020: starting the clock. PNAS 119:4e2115635118
    [Google Scholar]
  79. 79.
    Nadachowska-Brzyska K, Konczal M, Babik W. 2022. Navigating the temporal continuum of effective population size. Methods Ecol. Evol. 13:12241Comprehensive review of methods to infer historical Ne over a range of timescales.
    [Google Scholar]
  80. 80.
    Watterson GA. 1975. On the number of segregating sites in genetical models without recombination. Theor. Popul. Biol. 7:225676
    [Google Scholar]
  81. 81.
    Bourgeois YXC, Warren BH. 2021. An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes. Mol. Ecol. 30:23603671
    [Google Scholar]
  82. 82.
    Li H, Durbin R. 2011. Inference of human population history from individual whole-genome sequences. Nature 475:735749396First of several methods using the sequentially Markovian coalescent to infer historical variation in Ne using genome variation data.
    [Google Scholar]
  83. 83.
    Palacios JA, Wakeley J, Ramachandran S. 2015. Bayesian nonparametric inference of population size changes from sequential genealogies. Genetics 201:1281304
    [Google Scholar]
  84. 84.
    Speidel L, Forest M, Shi S, Myers SR. 2019. A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet. 51:9132129
    [Google Scholar]
  85. 85.
    Schiffels S, Durbin R. 2014. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46:891925
    [Google Scholar]
  86. 86.
    Terhorst J, Kamm JA, Song YS. 2017. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49:23039
    [Google Scholar]
  87. 87.
    Waples RS. 1989. A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics 121:237991
    [Google Scholar]
  88. 88.
    Hollenbeck CM, Portnoy DS, Gold JR. 2016. A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity 117:420716
    [Google Scholar]
  89. 89.
    Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. 2015. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front. Genet. 6:109
    [Google Scholar]
  90. 90.
    Santiago E, Novo I, Pardiñas AF, Saura M, Wang J, Caballero A 2020. Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol. Biol. Evol. 37:12364253
    [Google Scholar]
  91. 91.
    Waples RK, Larson WA, Waples RS. 2016. Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117:423340
    [Google Scholar]
  92. 92.
    Browning SR, Browning BL. 2015. Accurate non-parametric estimation of recent effective population size from segments of identity by descent. Am. J. Hum. Genet. 97:340418
    [Google Scholar]
  93. 93.
    Palamara PF, Lencz T, Darvasi A, Pe'er I 2012. Length distributions of identity by descent reveal fine-scale demographic history. Am. J. Hum. Genet. 91:580922
    [Google Scholar]
  94. 94.
    Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF. 2010. Genomic runs of homozygosity record population history and consanguinity. PLOS ONE 5:11e13996
    [Google Scholar]
  95. 95.
    Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. 2009. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLOS Genet. 5:10e1000695
    [Google Scholar]
  96. 96.
    Wakeley J. 2009. Coalescent Theory: An Introduction Greenwood Village, CO: Roberts & Co. Publ.
    [Google Scholar]
  97. 97.
    Wallberg A, Han F, Wellhagen G, Dahle B, Kawata M et al. 2014. A worldwide survey of genome sequence variation provides insight into the evolutionary history of the honeybee Apis mellifera. Nat. Genet. 46:10108188
    [Google Scholar]
  98. 98.
    Cao L-J, Song W, Chen J-C, Fan X-L, Hoffmann AA, Wei S-J. 2022. Population genomic signatures of the oriental fruit moth related to the Pleistocene climates. Commun. Biol. 5:142
    [Google Scholar]
  99. 99.
    Ortego J, Céspedes V, Millán A, Green AJ. 2021. Genomic data support multiple introductions and explosive demographic expansions in a highly invasive aquatic insect. Mol. Ecol. 30:174189203
    [Google Scholar]
  100. 100.
    McCoy RC, Garud NR, Kelley JL, Boggs CL, Petrov DA. 2014. Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population. Mol. Ecol. 23:113650
    [Google Scholar]
  101. 101.
    You M, Ke F, You S, Wu Z, Liu Q et al. 2020. Variation among 532 genomes unveils the origin and evolutionary history of a global insect herbivore. Nat. Commun. 11:2321
    [Google Scholar]
  102. 102.
    Elfekih S, Etter P, Tay WT, Fumagalli M, Gordon K et al. 2018. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLOS ONE 13:1e0190555
    [Google Scholar]
  103. 103.
    Soberon J. 1999. Linking biodiversity information sources. Trends Ecol. Evol. 14:7291
    [Google Scholar]
  104. 104.
    Gilbert MTP, Moore W, Melchior L, Worobey M. 2007. DNA extraction from dry museum beetles without conferring external morphological damage. PLOS ONE 2:3e272
    [Google Scholar]
  105. 105.
    Grewe F, Kronforst MR, Pierce NE, Moreau CS. 2021. Museum genomics reveals the Xerces blue butterfly (Glaucopsyche xerces) was a distinct species driven to extinction. Biol. Lett. 17:720210123
    [Google Scholar]
  106. 106.
    Díez-del-Molino D, Sánchez-Barreiro F, Barnes I, Gilbert MTP, Dalén L. 2018. Quantifying temporal genomic erosion in endangered species. Trends Ecol. Evol. 33:317685Insightful review into the use of genome data from historical samples to assess conservation status.
    [Google Scholar]
  107. 107.
    Gauthier J, Pajkovic M, Neuenschwander S, Kaila L, Schmid S et al. 2020. Museomics identifies genetic erosion in two butterfly species across the 20th century in Finland. Mol. Ecol. Resour. 20:51191205
    [Google Scholar]
  108. 108.
    Parejo M, Wragg D, Henriques D, Charrière J-D, Estonba A. 2020. Digging into the genomic past of Swiss honey bees by whole-genome sequencing museum specimens. Genome Biol. Evol. 12:12253551
    [Google Scholar]
  109. 109.
    Kimura M, Maruyama T, Crow JF. 1963. The mutation load in small populations. Genetics 48:130312
    [Google Scholar]
  110. 110.
    Henn BM, Botigué LR, Bustamante CD, Clark AG, Gravel S. 2015. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16:633343Detailed review of the population genetic forces influencing genetic load and how it can be estimated.
    [Google Scholar]
  111. 111.
    Klopfstein S, Currat M, Excoffier L. 2006. The fate of mutations surfing on the wave of a range expansion. Mol. Biol. Evol. 23:348290
    [Google Scholar]
  112. 112.
    Cingolani P, Platts A, Wang LL, Coon M, Nguyen T et al. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6:28092
    [Google Scholar]
  113. 113.
    Cooper GM, Stone EA, Asimenos G, NISC Comp. Seq. Program, Green ED et al. 2005. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15:790113
    [Google Scholar]
  114. 114.
    Kumar P, Henikoff S, Ng PC. 2009. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4:7107381
    [Google Scholar]
  115. 115.
    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A et al. 2010. A method and server for predicting damaging missense mutations. Nat. Methods 7:424849
    [Google Scholar]
  116. 116.
    Kleinman-Ruiz D, Lucena-Perez M, Villanueva B, Fernández J, Saveljev AP et al. 2022. Purging of deleterious burden in the endangered Iberian lynx. PNAS 119:11e2110614119
    [Google Scholar]
  117. 117.
    van der Valk T, de Manuel M, Marques-Bonet T, Guschanski K 2021. Estimates of genetic load suggest frequent purging of deleterious alleles in small populations. bioRxiv. 696831. https://doi.org/10.1101/696831
  118. 118.
    Pracana R, Burns R, Hammond RL, Haller BC, Wurm Y. 2022. Individual-based modeling of genome evolution in haplodiploid organisms. Genome Biol. Evol. 14:5evac062
    [Google Scholar]
  119. 119.
    Ross KG, Fletcher DJC. 1986. Diploid male production: a significant colony mortality factor in the fire ant Solenopsis invicta (Hymenoptera: Formicidae). Behav. Ecol. Sociobiol. 19:428391
    [Google Scholar]
  120. 120.
    Mattila ALK, Duplouy A, Kirjokangas M, Lehtonen R, Rastas P, Hanski I. 2012. High genetic load in an old isolated butterfly population. PNAS 109:37E2496505
    [Google Scholar]
  121. 121.
    Lenancker P, Hoffmann BD, Tay WT, Lach L. 2019. Strategies of the invasive tropical fire ant (Solenopsis geminata) to minimize inbreeding costs. Sci. Rep. 9:14566
    [Google Scholar]
  122. 122.
    Zayed A, Constantin ŞA, Packer L. 2007. Successful biological invasion despite a severe genetic load. PLOS ONE 2:9e868
    [Google Scholar]
  123. 123.
    Hebert PDN, Cywinska A, Ball SL, deWaard JR. 2003. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270:151231321
    [Google Scholar]
  124. 124.
    Nichols R. 2001. Gene trees and species trees are not the same. Trends Ecol. Evol. 16:735864
    [Google Scholar]
  125. 125.
    Bickford D, Lohman DJ, Sodhi NS, Ng PKL, Meier R et al. 2007. Cryptic species as a window on diversity and conservation. Trends Ecol. Evol. 22:314855
    [Google Scholar]
  126. 126.
    Hebert PDN, Penton EH, Burns JM, Janzen DH, Hallwachs W. 2004. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. PNAS 101:411481217
    [Google Scholar]
  127. 127.
    Christmas MJ, Jones JC, Olsson A, Wallerman O, Bunikis I et al. 2021. Genetic barriers to historical gene flow between cryptic species of Alpine bumblebees revealed by comparative population genomics. Mol. Biol. Evol. 38:8312643
    [Google Scholar]
  128. 128.
    Christmas MJ, Jones JC, Olsson A, Wallerman O, Bunikis I et al. 2022. A genomic and morphometric analysis of alpine bumblebees: ongoing reductions in tongue length but no clear genetic component. Mol. Ecol. 31:4111127
    [Google Scholar]
  129. 129.
    Sousa V, Hey J. 2013. Understanding the origin of species with genome-scale data: modelling gene flow. Nat. Rev. Genet. 14:640414
    [Google Scholar]
  130. 130.
    Heliconius Genome Consort 2012. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487:74059498
    [Google Scholar]
  131. 131.
    Miles A, Harding NJ, Bottà G, Clarkson CS, Antão T et al. 2017. Genetic diversity of the African malaria vector Anopheles gambiae. Nature 552:768396100
    [Google Scholar]
  132. 132.
    Galtier N. 2019. Delineating species in the speciation continuum: a proposal. Evol. Appl. 12:465763
    [Google Scholar]
  133. 133.
    Hey J, Waples RS, Arnold ML, Butlin RK, Harrison RG. 2003. Understanding and confronting species uncertainty in biology and conservation. Trends Ecol. Evol. 18:11597603
    [Google Scholar]
  134. 134.
    Redding DW, Mooers AØ. 2006. Incorporating evolutionary measures into conservation prioritization. Conserv. Biol. 20:6167078
    [Google Scholar]
  135. 135.
    Sistri G, Menchetti M, Santini L, Pasquali L, Sapienti S et al. 2022. The isolated Erebia pandrose Apennine population is genetically unique and endangered by climate change. Insect Conserv. Divers. 15:113648
    [Google Scholar]
  136. 136.
    Podsiadlowski L, Tunström K, Espeland M, Wheat CW. 2021. The genome assembly and annotation of the Apollo butterfly Parnassius apollo, a flagship species for conservation biology. Genome Biol Evol. 13:8evab122
    [Google Scholar]
  137. 137.
    Dhuyvetter H, Gaublomme E, Verdyck P, Desender K. 2005. Genetic differentiation among populations of the salt marsh beetle Pogonus littoralis (Coleoptera: Carabidae): a comparison between Atlantic and Mediterranean populations. J. Hered. 96:438187
    [Google Scholar]
  138. 138.
    Crozier L, Dwyer G. 2006. Combining population-dynamic and ecophysiological models to predict climate-induced insect range shifts. Am. Nat. 167:685366
    [Google Scholar]
  139. 139.
    Broquet T, Petit EJ. 2009. Molecular estimation of dispersal for ecology and population genetics. Annu. Rev. Ecol. Evol. Syst. 40:193216
    [Google Scholar]
  140. 140.
    Waldvogel A-M, Feldmeyer B, Rolshausen G, Exposito-Alonso M, Rellstab C et al. 2020. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4:1418Stimulating review of the ways population genomics can be used to predict species response to climate change incorporating adaptive potential.
    [Google Scholar]
  141. 141.
    Savolainen O, Lascoux M, Merilä J. 2013. Ecological genomics of local adaptation. Nat. Rev. Genet. 14:1180720
    [Google Scholar]
  142. 142.
    Novembre J, Di Rienzo A. 2009. Spatial patterns of variation due to natural selection in humans. Nat. Rev. Genet. 10:74555
    [Google Scholar]
  143. 143.
    Coop G, Witonsky D, Rienzo AD, Pritchard JK. 2010. Using environmental correlations to identify loci underlying local adaptation. Genetics 185:4141123
    [Google Scholar]
  144. 144.
    Günther T, Coop G. 2013. Robust identification of local adaptation from allele frequencies. Genetics 195:120520
    [Google Scholar]
  145. 145.
    Frichot E, Schoville SD, Bouchard G, François O. 2013. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30:7168799
    [Google Scholar]
  146. 146.
    Capblancq T, Forester BR. 2021. Redundancy analysis: a Swiss army knife for landscape genomics. Methods Ecol. Evol. 12:122298309
    [Google Scholar]
  147. 147.
    Adrion JR, Hahn MW, Cooper BS. 2015. Revisiting classic clines in Drosophila melanogaster in the age of genomics. Trends Genet. 31:843444
    [Google Scholar]
  148. 148.
    Turner TL, Levine MT, Eckert ML, Begun DJ. 2008. Genomic analysis of adaptive differentiation in Drosophila melanogaster. Genetics 179:145573
    [Google Scholar]
  149. 149.
    Machado HE, Bergland AO, Taylor R, Tilk S, Behrman E et al. 2021. Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in Drosophila. eLife 10:e67577
    [Google Scholar]
  150. 150.
    Balanyá J, Oller JM, Huey RB, Gilchrist GW, Serra L. 2006. Global genetic change tracks global climate warming in Drosophila subobscura. Science 313:5794177375
    [Google Scholar]
  151. 151.
    Calfee E, Agra MN, Palacio MA, Ramírez SR, Coop G. 2020. Selection and hybridization shaped the rapid spread of African honey bee ancestry in the Americas. PLOS Genet. 16:10e1009038
    [Google Scholar]
  152. 152.
    Wallberg A, Schöning C, Webster MT, Hasselmann M. 2017. Two extended haplotype blocks are associated with adaptation to high altitude habitats in East African honey bees. PLOS Genet. 13:5e1006792
    [Google Scholar]
  153. 153.
    Christmas MJ, Wallberg A, Bunikis I, Olsson A, Wallerman O, Webster MT. 2019. Chromosomal inversions associated with environmental adaptation in honeybees. Mol. Ecol. 28:6135874
    [Google Scholar]
  154. 154.
    Montero-Mendieta S, Tan K, Christmas MJ, Olsson A, Vilà C et al. 2019. The genomic basis of adaptation to high-altitude habitats in the eastern honey bee (Apis cerana). Mol. Ecol. 28:474660
    [Google Scholar]
  155. 155.
    Pimsler ML, Oyen KJ, Herndon JD, Jackson JM, Strange JP et al. 2020. Biogeographic parallels in thermal tolerance and gene expression variation under temperature stress in a widespread bumble bee. Sci. Rep. 10:117063
    [Google Scholar]
  156. 156.
    Chen Y, Liu Z, Régnière J, Vasseur L, Lin J et al. 2021. Large-scale genome-wide study reveals climate adaptive variability in a cosmopolitan pest. Nat. Commun. 12:7206
    [Google Scholar]
  157. 157.
    Gamboa M, Watanabe K. 2019. Genome-wide signatures of local adaptation among seven stoneflies species along a nationwide latitudinal gradient in Japan. BMC Genom. 20:84
    [Google Scholar]
  158. 158.
    Dudaniec RY, Yong CJ, Lancaster LT, Svensson EI, Hansson B. 2018. Signatures of local adaptation along environmental gradients in a range-expanding damselfly (Ischnura elegans). Mol. Ecol. 27:11257693
    [Google Scholar]
  159. 159.
    Fouet C, Atkinson P, Kamdem C. 2018. Human interventions: driving forces of mosquito evolution. Trends Parasitol. 34:212739
    [Google Scholar]
  160. 160.
    Bass C, Denholm I, Williamson MS, Nauen R. 2015. The global status of insect resistance to neonicotinoid insecticides. Pestic. Biochem. Physiol. 121:7887
    [Google Scholar]
  161. 161.
    Straub L, Strobl V, Neumann P. 2020. The need for an evolutionary approach to ecotoxicology. Nat. Ecol. Evol. 4:895
    [Google Scholar]
  162. 162.
    Pélissié B, Chen YH, Cohen ZP, Crossley MS, Hawthorne DJ et al. 2022. Genome resequencing reveals rapid, repeated evolution in the Colorado potato beetle. Mol. Biol. Evol. 39:2msac016
    [Google Scholar]
  163. 163.
    Kiani M, Fu Z, Szczepaniec A. 2022. ddRAD sequencing identifies pesticide resistance-related loci and reveals new insights into genetic structure of Bactericera cockerelli as a plant pathogen vector. Insects 13:3257
    [Google Scholar]
  164. 164.
    Singh KS, Cordeiro EMG, Troczka BJ, Pym A, Mackisack J et al. 2021. Global patterns in genomic diversity underpinning the evolution of insecticide resistance in the aphid crop pest Myzus persicae. Commun. Biol. 4:847
    [Google Scholar]
  165. 165.
    Valencia-Montoya WA, Elfekih S, North HL, Meier JI, Warren IA et al. 2020. Adaptive introgression across semipermeable species boundaries between local Helicoverpa zea and invasive Helicoverpa armigera moths. Mol. Biol. Evol. 37:9256883
    [Google Scholar]
  166. 166.
    Hou Z, Wei C 2019. De novo comparative transcriptome analysis of a rare cicada, with identification of candidate genes related to adaptation to a novel host plant and drier habitats. BMC Genom. 20:182
    [Google Scholar]
  167. 167.
    Simon J-C, d'Alençon E, Guy E, Jacquin-Joly E, Jaquiéry J et al. 2015. Genomics of adaptation to host-plants in herbivorous insects. Brief. Funct. Genom. 14:641323
    [Google Scholar]
  168. 168.
    Oppenheim SJ, Gould F, Hopper KR. 2012. The genetic architecture of a complex ecological trait: host plant use in the specialist moth, Heliothis subflexa. Evolution 66:11333651
    [Google Scholar]
  169. 169.
    Caillaud MC, Via S. 2012. Quantitative genetics of feeding behavior in two ecological races of the pea aphid, Acyrthosiphon pisum. . Heredity 108:321118
    [Google Scholar]
  170. 170.
    Soria-Carrasco V, Gompert Z, Comeault AA, Farkas TE, Parchman TL et al. 2014. Stick insect genomes reveal natural selection's role in parallel speciation. Science 344:618573842
    [Google Scholar]
  171. 171.
    Carvell C, Bourke AFG, Dreier S, Freeman SN, Hulmes S et al. 2017. Bumblebee family lineage survival is enhanced in high-quality landscapes. Nature 543:764654749
    [Google Scholar]
  172. 172.
    Traynor KS, Mondet F, de Miranda JR, Techer M, Kowallik V et al. 2020. Varroa destructor: a complex parasite, crippling honey bees worldwide. Trends Parasitol. 36:7592606
    [Google Scholar]
  173. 173.
    Mondet F, Beaurepaire A, McAfee A, Locke B, Alaux C et al. 2020. Honey bee survival mechanisms against the parasite Varroa destructor: a systematic review of phenotypic and genomic research efforts. Int. J. Parasitol. 50:643347
    [Google Scholar]
  174. 174.
    Broeckx BJG, De Smet L, Blacquière T, Maebe K, Khalenkow M et al. 2019. Honey bee predisposition of resistance to ubiquitous mite infestations. Sci. Rep. 9:7794
    [Google Scholar]
  175. 175.
    Wood TJ, Michez D, Paxton RJ, Drossart M, Neumann P et al. 2020. Managed honey bees as a radar for wild bee decline?. Apidologie 51:6110016
    [Google Scholar]
  176. 176.
    Sackton TB. 2019. Comparative genomics and transcriptomics of host-pathogen interactions in insects: evolutionary insights and future directions. Curr. Opin. Insect Sci. 31:10613
    [Google Scholar]
  177. 177.
    Des Roches S, Pendleton LH, Shapiro B, Palkovacs EP. 2021. Conserving intraspecific variation for nature's contributions to people. Nat. Ecol. Evol. 5:57482
    [Google Scholar]
  178. 178.
    Wellenreuther M, Bernatchez L. 2018. Eco-evolutionary genomics of chromosomal inversions. Trends Ecol. Evol. 33:642740
    [Google Scholar]
  179. 179.
    Rellstab C, Dauphin B, Exposito-Alonso M. 2021. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14:5120212
    [Google Scholar]
  180. 180.
    Fuller ZL, Mocellin VJL, Morris LA, Cantin N, Shepherd J et al. 2020. Population genetics of the coral Acropora millepora: toward genomic prediction of bleaching. Science 369:6501eaba4674Important example of the use of genomics to predict response to climate change.
    [Google Scholar]
  181. 181.
    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. 2:235258
    [Google Scholar]
  182. 182.
    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 359:63718386
    [Google Scholar]
  183. 183.
    Beng KC, Tomlinson KW, Shen XH, Surget-Groba Y, Hughes AC et al. 2016. The utility of DNA metabarcoding for studying the response of arthropod diversity and composition to land-use change in the tropics. Sci. Rep. 6:124965
    [Google Scholar]
  184. 184.
    Corlett RT. 2017. A bigger toolbox: biotechnology in biodiversity conservation. Trends Biotechnol. 35:15565
    [Google Scholar]
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